From 58185c0ab90d76f4f451c55a2591001d7ec0cde4 Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Tue, 1 Jul 2025 15:04:17 -0400 Subject: [PATCH 01/22] fix typos in TOC/simulation section, added skeleton for book intro and simulation overview, restructured simulation sections --- book/_toc.yml | 4 ++-- book/intro.md | 13 +++++++++++++ book/simulation/Birth_Month_Simulation.ipynb | 11 +++++++---- book/simulation/Simulating_Dice.ipynb | 20 ++++++++++++++------ book/simulation/overview.md | 6 ++++-- 5 files changed, 40 insertions(+), 14 deletions(-) diff --git a/book/_toc.yml b/book/_toc.yml index e83a25e..9a3346a 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -2,13 +2,13 @@ format: jb-book root: intro.md parts: - - caption: Subjects + - caption: Contents chapters: - file: simulation/overview.md sections: - file: simulation/Simulating_Dice.ipynb - file: simulation/Birth_Month_Simulation.ipynb - - caption: Miscallaneous + - caption: Miscellaneous chapters: - file: references.md #- file: changelog.md diff --git a/book/intro.md b/book/intro.md index 38a7b66..765ec64 100644 --- a/book/intro.md +++ b/book/intro.md @@ -1,3 +1,16 @@ (intro)= # Probability and Statistics - Code Companion This online book is designed to complement the Probability and Statistics courses taught at TU Delft. It explains how the visualisations and data analyses used in the course were created and guides you in making your own. + +```{tip} +You can interact with coding sections of the book by clicking the rocket icon ({fa}`rocket`) found in the top right of the page. +``` + +## Additional Resources + +## Reporting Mistakes + +## Questions and Answers + +## Book Layout + diff --git a/book/simulation/Birth_Month_Simulation.ipynb b/book/simulation/Birth_Month_Simulation.ipynb index 5dd8192..1c21191 100644 --- a/book/simulation/Birth_Month_Simulation.ipynb +++ b/book/simulation/Birth_Month_Simulation.ipynb @@ -6,7 +6,8 @@ "source": [ "# Birth Month Simulation\n", "\n", - "We can use the built in random choice feature of numpy to simulate non-uniformly distributed events. For instance below we consider the situation where we want to simulate the choice of a costumer at a chips shop. The costumer can chooce to have mayonaise, ketchup, curry or peanut sauce on their chips. We simulate the choice the costumer makes by assigning each choice a certain probability. " + "## Chips Shop Order Choice\n", + "We can use the built in random choice feature of [numpy](https://numpy.org/doc/) to simulate non-uniformly distributed events. For instance, below we consider the situation where we want to simulate the choice of a customer at a chips shop. The customer can choose to have mayonnaise, ketchup, curry or peanut sauce on their chips. We simulate the choice the costumer makes by assigning each choice a certain probability. " ] }, { @@ -42,7 +43,7 @@ "metadata": {}, "outputs": [], "source": [ - "food = [\"mayonaise\", \"ketchup\", \"curry\", \"peanut sauce\"]\n", + "food = [\"mayonnaise\", \"ketchup\", \"curry\", \"peanut sauce\"]\n", "p = [0.6, 0.1, 0.15, 0.15]\n", "food_choice = np.random.choice(food, p=p)\n", "print(food_choice)" @@ -52,7 +53,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now let's consider simulation of the birth months. So we will simulate a town of people together with their birth months. We use three different models for the birth months distributions. Namely, the uniform distribution over months, a uniform model over days that does not take leap days into account and a uniform model over days that does take leap days into account. " + "## Birth Month\n", + "\n", + "Now let's consider simulation of the birth months. We will simulate a town of people together with their birth months. We use three different models for the birth months distributions. Namely, the uniform distribution over months, a uniform model over days that does not take leap days into account and a uniform model over days that does take leap days into account. " ] }, { @@ -117,7 +120,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Finally let's have a look at our simulated distribution. For more on plotting have a look at the visualiation chapter. Below we load our simulated data into a dataframe and subsquently plot the proportions of each model into a histogram. " + "Finally let's have a look at our simulated distribution. For more on plotting have a look at the visualization chapter. Below we load our simulated data into a dataframe and subsequently plot the proportions of each model into a histogram. " ] }, { diff --git a/book/simulation/Simulating_Dice.ipynb b/book/simulation/Simulating_Dice.ipynb index 9319dd2..ddcde33 100644 --- a/book/simulation/Simulating_Dice.ipynb +++ b/book/simulation/Simulating_Dice.ipynb @@ -33,7 +33,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " The most basic building blocks are uniform random variables. We will gloss over the mechanics of how random number generators are produced and simply call them, using the `numpy` package. \n", + " ## Uniform Random Variables\n", + " \n", + " The most basic building blocks are uniform random variables. We will gloss over the mechanics of how random number generators are produced and simply call them, using the [`numpy`](https://numpy.org/doc/) package. \n", "Below is our first simulated $X\\sim U(0,1)$ random variable. " ] }, @@ -52,9 +54,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The function np.random.uniform can be supplied two parameters, a lower limit and an upper limit, which are by default set to $0$ and $1$. Above you can enter these parameter to produce instead a uniformly distributed real number between for instance $-1$ and $4$ by entering $-1, 4$ in the brackets. \n", + "The function `np.random.uniform` can be supplied two parameters, a lower limit ($\\alpha$) and an upper limit ($\\beta$), which are by default set to $0$ and $1$. You can enter these parameter to produce a uniformly distributed real number between $\\alpha$ and $\\beta$. For instance, you can produce a unfiromly distributed real number between $-1$ and $4$ using the following line of code:\n", "\n", - "We can use this uniformly distributed random variable to simulate various other distributions. For instance we may simulate a six sided die:" + "```python\n", + "np.random.uniform(-1,4)\n", + "```\n", + "\n", + "We can use this uniformly distributed random variable to simulate various other distributions. For instance we may simulate a six-sided die:" ] }, { @@ -72,9 +78,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Indeed in the code above we randomly generate a uniformly distributed number between 1 and 7 then round down to the nearest integer, which produces a(n ideal) die. You may want to try to change the code to simulate a $4$ or $8$ sided die. \n", + "Indeed in the code above we randomly generate a uniformly distributed number between $1$ and $7$ then round down to the nearest integer, which produces a(n ideal) die. You may want to try to change the code to simulate a $4$ or $8$ sided die. \n", + "\n", + "Very often we will need to simulate not one die, but many dice at the same time. The third parameter of the uniform distribution is the size. We may use this parameter to produce an array of many random numbers at once. Each random number is independently uniformly distributed. \n", "\n", - "Very often we will need to simulate not one die, but many dice at the same time. The third parameter of the uniform distribution is the size. We may use this parameter to produce an array of many random numbers at once. Each random number is independently uniformly distributed. " + "The code below simulates rolling a six-sided die ten times." ] }, { @@ -117,7 +125,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Finally we can now simulate not just individual die rolls, but also sums of dice rolls leading to simulations of non-uniformly distributed random variables. For example below we simulate many rolls of a pair of $4$ sided die rolls and check that we obtain the expected distribution of sums. " + "Finally we can now simulate not just individual die rolls, but also sums of dice rolls leading to simulations of non-uniformly distributed random variables. For example, below we simulate many rolls of a pair of four-sided die rolls and check that we obtain the expected distribution of sums. " ] }, { diff --git a/book/simulation/overview.md b/book/simulation/overview.md index fe4cf2c..9d4a0d1 100644 --- a/book/simulation/overview.md +++ b/book/simulation/overview.md @@ -1,5 +1,7 @@ # Simulation + -This is the file simulation/overview.md . -Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. \ No newline at end of file + +## Chapter Overview + \ No newline at end of file From 960602094b5efda1eb02edee75d396443849029f Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Wed, 2 Jul 2025 11:19:34 -0400 Subject: [PATCH 02/22] add explanation for chip shop example for clarity, update spelling to use American english --- book/intro.md | 2 +- book/simulation/Birth_Month_Simulation.ipynb | 15 +++++++++------ book/simulation/overview.md | 4 ++-- 3 files changed, 12 insertions(+), 9 deletions(-) diff --git a/book/intro.md b/book/intro.md index 765ec64..d8aafd5 100644 --- a/book/intro.md +++ b/book/intro.md @@ -1,6 +1,6 @@ (intro)= # Probability and Statistics - Code Companion -This online book is designed to complement the Probability and Statistics courses taught at TU Delft. It explains how the visualisations and data analyses used in the course were created and guides you in making your own. +This online book is designed to complement the Probability and Statistics courses taught at TU Delft. It explains how the visualizations and data analyses used in the course were created and guides you in making your own. ```{tip} You can interact with coding sections of the book by clicking the rocket icon ({fa}`rocket`) found in the top right of the page. diff --git a/book/simulation/Birth_Month_Simulation.ipynb b/book/simulation/Birth_Month_Simulation.ipynb index 1c21191..23d4fe8 100644 --- a/book/simulation/Birth_Month_Simulation.ipynb +++ b/book/simulation/Birth_Month_Simulation.ipynb @@ -6,8 +6,9 @@ "source": [ "# Birth Month Simulation\n", "\n", - "## Chips Shop Order Choice\n", - "We can use the built in random choice feature of [numpy](https://numpy.org/doc/) to simulate non-uniformly distributed events. For instance, below we consider the situation where we want to simulate the choice of a customer at a chips shop. The customer can choose to have mayonnaise, ketchup, curry or peanut sauce on their chips. We simulate the choice the costumer makes by assigning each choice a certain probability. " + "We can use the built in random choice feature of [numpy](https://numpy.org/doc/) to simulate non-uniformly distributed events. \n", + "\n", + "To illustrate the `np.random.choice` functionality, consider a toy example where we want to simulate the choice of a customer at a fries shop. The customer can choose to have mayonnaise, ketchup, curry or peanut sauce on their fries. We simulate the choice the customer makes by assigning each choice a certain probability. " ] }, { @@ -53,9 +54,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Birth Month\n", - "\n", - "Now let's consider simulation of the birth months. We will simulate a town of people together with their birth months. We use three different models for the birth months distributions. Namely, the uniform distribution over months, a uniform model over days that does not take leap days into account and a uniform model over days that does take leap days into account. " + "Now let's consider simulation of the birth months. We will simulate a town of people together with their birth months. We use three different models for the birth months distributions: the uniform distribution over months, a uniform model over days that does not take leap days into account, and a uniform model over days that does take leap days into account. " ] }, { @@ -120,7 +119,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Finally let's have a look at our simulated distribution. For more on plotting have a look at the visualization chapter. Below we load our simulated data into a dataframe and subsequently plot the proportions of each model into a histogram. " + "Finally let's have a look at our simulated distribution. Below we load our simulated data into a dataframe and subsequently plot the proportions of each model into a histogram. \n", + "\n", + "```{tip}\n", + "For more on plotting have a look at the visualization chapter.\n", + "```{tip}" ] }, { diff --git a/book/simulation/overview.md b/book/simulation/overview.md index 9d4a0d1..4cdcd18 100644 --- a/book/simulation/overview.md +++ b/book/simulation/overview.md @@ -1,7 +1,7 @@ # Simulation - + ## Chapter Overview - \ No newline at end of file + \ No newline at end of file From e07c645111a874ad72f774482edca08976080731 Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Sat, 5 Jul 2025 10:03:57 -0400 Subject: [PATCH 03/22] add initial draft of birth estimation section --- book/_toc.yml | 3 + book/estimation/Birth_Month_Estimation.ipynb | 222 + book/estimation/data/birth-records.csv | 53046 +++++++++++++++++ book/estimation/overview.md | 7 + 4 files changed, 53278 insertions(+) create mode 100644 book/estimation/Birth_Month_Estimation.ipynb create mode 100644 book/estimation/data/birth-records.csv create mode 100644 book/estimation/overview.md diff --git a/book/_toc.yml b/book/_toc.yml index 9a3346a..de50192 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -8,6 +8,9 @@ parts: sections: - file: simulation/Simulating_Dice.ipynb - file: simulation/Birth_Month_Simulation.ipynb + - file: estimation/overview.md + sections: + - file: estimation/Birth_Month_Estimation.ipynb - caption: Miscellaneous chapters: - file: references.md diff --git a/book/estimation/Birth_Month_Estimation.ipynb b/book/estimation/Birth_Month_Estimation.ipynb new file mode 100644 index 0000000..050f6b3 --- /dev/null +++ b/book/estimation/Birth_Month_Estimation.ipynb @@ -0,0 +1,222 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9f0496fc", + "metadata": {}, + "source": [ + "# Birth Months\n", + "\n", + "In this chapter we will be estimating the probability mass function of birth months. We will be doing this using a dataset. The first step to analyze this data is to perform *data wrangling*, which is the process of cleaning the dataset and (when necessary) converting it to a suitable format. In this case we will be isolating the birth month and getting several data-sets of size $100$ that we can compare later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7be061d3", + "metadata": { + "tags": [ + "thebe-remove-input-init" + ] + }, + "outputs": [], + "source": [ + "import micropip\n", + "\n", + "await micropip.install(\"seaborn\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7856f2b4", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "months = [\n", + " \"January\",\n", + " \"February\",\n", + " \"March\",\n", + " \"April\",\n", + " \"May\",\n", + " \"June\",\n", + " \"July\",\n", + " \"August\",\n", + " \"September\",\n", + " \"October\",\n", + " \"November\",\n", + " \"December\",\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cacbeec", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Read in the data and extract the birth month\n", + "df = pd.read_csv(\"data/birth-records.csv\")\n", + "df[\"ChildDOB\"] = df[\"ChildDOB\"].str.replace(\"-\", \"/\", regex=False)\n", + "\n", + "df[\"ChildDOB\"] = pd.to_datetime(\n", + " df[\"ChildDOB\"], infer_datetime_format=True, dayfirst=False, errors=\"coerce\"\n", + ")\n", + "\n", + "df[\"birth_month\"] = df[\"ChildDOB\"].dt.month_name()\n", + "\n", + "# Sample three times from the dataset\n", + "sample1 = df[\"birth_month\"].sample(n=100, random_state=42)\n", + "sample2 = df[\"birth_month\"].sample(n=100, random_state=7)\n", + "sample3 = df[\"birth_month\"].sample(n=100, random_state=99)" + ] + }, + { + "cell_type": "markdown", + "id": "b8114917", + "metadata": {}, + "source": [ + "For each dataset we now estimate the probability mass function. We will be estimating this using the proportion. The result will be plotted as a normalized histogram." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8afa175", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "df_sample1 = pd.DataFrame({\"Month\": sample1.values})\n", + "df_sample2 = pd.DataFrame({\"Month\": sample2.values})\n", + "df_sample3 = pd.DataFrame({\"Month\": sample3.values})\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "plt.subplot(1, 3, 1)\n", + "sns.countplot(x=\"Month\", data=df_sample1, order=months, stat=\"proportion\")\n", + "plt.title(\"Distribution of sample 1\")\n", + "plt.xlabel(\"Month\")\n", + "plt.ylabel(\"Proportion\")\n", + "plt.xticks(rotation=45)\n", + "\n", + "plt.subplot(1, 3, 2)\n", + "sns.countplot(x=\"Month\", data=df_sample2, order=months, stat=\"proportion\")\n", + "plt.title(\"Distribution of sample 1\")\n", + "plt.xlabel(\"Month\")\n", + "plt.ylabel(\"Proportion\")\n", + "plt.xticks(rotation=45)\n", + "\n", + "plt.subplot(1, 3, 3)\n", + "sns.countplot(x=\"Month\", data=df_sample3, order=months, stat=\"proportion\")\n", + "plt.title(\"Distribution of sample 1\")\n", + "plt.xlabel(\"Month\")\n", + "plt.ylabel(\"Proportion\")\n", + "plt.xticks(rotation=45)\n", + "\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "708253d1", + "metadata": {}, + "source": [ + "We can then compare this to more naïve models considered in the simulation chapter and to the estimate calculated when considering the entire dataset in the code below.\n", + "\n", + "Feel free to play around with the size used in the naïve model and the sample from the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d81f79be", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# naive model for months\n", + "naive_months = np.random.choice(months, size=20000, p=[1 / 12 for i in range(12)])\n", + "\n", + "df_naive_months = pd.DataFrame(naive_months, columns=[\"Month\"])\n", + "\n", + "# estimate using entire dataset\n", + "df[\"birth_month\"]\n", + "entire_dataset = pd.DataFrame({\"Month\": df[\"birth_month\"].values})\n", + "\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "\n", + "plt.subplot(1, 3, 1)\n", + "sns.countplot(x=\"Month\", data=df_naive_months, order=months, stat=\"proportion\")\n", + "plt.title(\"Distribution: Naive Uniform on Months\")\n", + "plt.xlabel(\"Month\")\n", + "plt.ylabel(\"Proportion\")\n", + "plt.xticks(rotation=45)\n", + "\n", + "\n", + "plt.subplot(1, 3, 2)\n", + "sns.countplot(\n", + " x=\"Month\",\n", + " data=entire_dataset,\n", + " order=months,\n", + " stat=\"proportion\",\n", + ")\n", + "plt.title(\"Distribution: Entire Dataset\")\n", + "plt.xlabel(\"Month\")\n", + "plt.ylabel(\"Proportion\")\n", + "plt.xticks(rotation=45)\n", + "\n", + "\n", + "plt.subplot(1, 3, 3)\n", + "sns.countplot(\n", + " x=\"Month\",\n", + " data=df_sample1,\n", + " order=months,\n", + " stat=\"proportion\",\n", + ")\n", + "plt.title(\"Distribution: Sample of Dataset with n=100\")\n", + "plt.xlabel(\"Month\")\n", + "plt.ylabel(\"Proportion\")\n", + "plt.xticks(rotation=45)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e1851d05", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/estimation/data/birth-records.csv b/book/estimation/data/birth-records.csv new file mode 100644 index 0000000..649323b --- /dev/null +++ b/book/estimation/data/birth-records.csv @@ -0,0 +1,53046 @@ +ChildDOB +1-3-1920 +1/28/2008 +1/28/2008 +1/29/2008 +1/28/2008 +1/28/2008 +1/18/2010 +1/29/2008 +1/29/2008 +1/29/2008 +1/27/1929 +1/30/2008 +1/31/2008 +1/31/2008 +1/31/2008 +1/30/2008 +1-7-2005 +1/29/2008 +1-5-1997 +1/23/2007 +1/19/2010 +1-6-2010 +1/26/2009 +1-11-2010 +1/19/2004 +1/27/2009 +1/17/2002 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+12-6-2016 +12-8-2017 +12/26/2014 +12-3-2014 +12/23/2014 +12/24/2016 +12-1-2016 +12-2-2007 +12/17/2015 +12/17/2015 +12/14/2016 +12/23/2014 +12/28/2015 +12-5-1942 +12/23/2010 +12/15/2013 +12/18/2011 +12/28/1998 +12/28/1998 +12/24/2002 +12/15/2015 +12/23/2015 +12/16/2016 +12-6-2008 +12-6-2008 +12-5-2011 +12/22/2008 +12-12-1940 +12-7-2003 +12/14/2017 +12/26/2015 diff --git a/book/estimation/overview.md b/book/estimation/overview.md new file mode 100644 index 0000000..5b1b7f2 --- /dev/null +++ b/book/estimation/overview.md @@ -0,0 +1,7 @@ +# Estimation + + + + +## Chapter Overview + \ No newline at end of file From c32e1c12f5a8ee88d0bc9254da04de2c9c14b055 Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Sat, 5 Jul 2025 12:12:13 -0400 Subject: [PATCH 04/22] add initial draft of 911 call estimation page --- book/_toc.yml | 1 + book/estimation/911_Calls_Estimation.ipynb | 221 + book/estimation/data/911_Calls.csv | 5933 ++++++++++++++++++++ 3 files changed, 6155 insertions(+) create mode 100644 book/estimation/911_Calls_Estimation.ipynb create mode 100644 book/estimation/data/911_Calls.csv diff --git a/book/_toc.yml b/book/_toc.yml index de50192..0db1250 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -11,6 +11,7 @@ parts: - file: estimation/overview.md sections: - file: estimation/Birth_Month_Estimation.ipynb + - file: estimation/911_Calls_Estimation.ipynb - caption: Miscellaneous chapters: - file: references.md diff --git a/book/estimation/911_Calls_Estimation.ipynb b/book/estimation/911_Calls_Estimation.ipynb new file mode 100644 index 0000000..9327a07 --- /dev/null +++ b/book/estimation/911_Calls_Estimation.ipynb @@ -0,0 +1,221 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9f0496fc", + "metadata": {}, + "source": [ + "# 911 Calls\n", + "\n", + "In this chapter we will be using data on 911 calls in Fernhaven to estimate the probability of having no 911 calls in a minute. This distribution is modeled using a Poisson distribution with probability mass function \n", + "\n", + "$$\n", + " p(k)=\\frac{\\mu^k}{k!}e^{-\\mu}\n", + "$$ \n", + "where $\\mu$ is the expected number of calls in one minute.\n", + "\n", + "We will be covering two methods of estimation: proportion and method of moments/maximum likelihood estimate (MLE).\n", + "\n", + "The first step is to perform *data wrangling* on the dataset to get several datasets of the number of calls per minute with $n=60$ (i.e. an hour long). In order to assume the same distribution across these datasets we will use the same time of day for each dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7856f2b4", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import math\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0d2f978", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Read in the data and extract calls between 6 and 7\n", + "df = pd.read_csv(\"data/911_Calls.csv\")\n", + "\n", + "df['datetime'] = pd.to_datetime(df['Date'] + ' ' + df['Call time'])\n", + " \n", + "df['date'] = df['datetime'].dt.date.astype(str)\n", + "df['hour'] = df['datetime'].dt.hour\n", + "df['minute'] = df['datetime'].dt.minute\n", + "\n", + "df_hour = df[df['hour'] == 6]\n", + "counts = (\n", + " df_hour\n", + " .groupby(['date', 'minute'])\n", + " .size()\n", + " .reset_index(name='count')\n", + " )\n", + "\n", + "hourly_counts = {}\n", + "for date, grp in counts.groupby('date'):\n", + " series = grp.set_index('minute')['count']\n", + " full = series.reindex(range(60), fill_value=0)\n", + " hourly_counts[date] = full.tolist()" + ] + }, + { + "cell_type": "markdown", + "id": "74f8f04d", + "metadata": {}, + "source": [ + "Now that we have our datasets we can compute our estimates. \n", + "\n", + "The first estimate we will compute is the proportion of minutes with no calls in each dataset ($\\hat{p}$). \n", + "\n", + "$$\n", + " \\hat{p}=\\frac{\\text{number of minutes with 0 calls}}{60}\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea3c1dad", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "p_hat = [\n", + " counts.count(0) / 60.0\n", + " for counts in hourly_counts.values()\n", + "]\n", + "\n", + "p_hat" + ] + }, + { + "cell_type": "markdown", + "id": "b270c19e", + "metadata": {}, + "source": [ + "The second estimate is the MLE ($e^{-\\hat{\\mu}}$). To do so, we need to calculate an estimate for $\\mu$. For each dataset we calculate $\\hat{\\mu}$ with the formula: \n", + "\n", + "$$\n", + " \\hat{\\mu}=\\frac{\\text{number of calls}}{60}=\\frac{\\text{sum of calls per minute}}{60}\n", + "$$\n", + "\n", + "Once we have calculated an estimate for $\\mu$, we can use the probability mass function of the Poisson distribution where $k=0$ to calculate $e^{-\\hat{\\mu}}$. \n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e940e960", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "mle = [\n", + " math.exp(-1 * (sum(counts) / 60.0))\n", + " for counts in hourly_counts.values()\n", + "]\n", + "\n", + "mle" + ] + }, + { + "cell_type": "markdown", + "id": "946682b2", + "metadata": {}, + "source": [ + "We can now make histograms of the obtained estimates for both cases to estimate the distribution and indicate the deviation from the true parameter. The true parameter in this case will be obtained using the full data-set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b91c8d1", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# compute the true parameter over the full dataset\n", + "call_counts_full_dataset = (\n", + " df\n", + " .groupby(['date', 'hour', 'minute'])\n", + " .size()\n", + " .reset_index(name='count')\n", + " )\n", + "full_counts = []\n", + "for (_, _), grp in call_counts_full_dataset.groupby(['date', 'hour']):\n", + " ser = grp.set_index('minute')['count']\n", + " full_counts.extend(\n", + " ser.reindex(range(60), fill_value=0).tolist()\n", + " )\n", + "\n", + "true_parameter = full_counts.count(0) / len(full_counts)\n", + "print(f\"Estimated p (full dataset) = {true_parameter}\")\n", + "\n", + "# plot estimates\n", + "plt.figure(figsize=(15, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(p_hat)\n", + "plt.title(\"Estimation $\\hat{p}$\")\n", + "plt.ylabel(\"frequency\")\n", + "\n", + "plt.axvline(x=true_parameter, color='r', linestyle='--')\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(mle)\n", + "plt.title(\"Estimation $e^{-\\mu}$\")\n", + "plt.ylabel(\"frequency\")\n", + "\n", + "plt.axvline(x=true_parameter, color='r', linestyle='--')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fa8fe489", + "metadata": {}, + "source": [ + "Given these two estimators, we need to determine the best one. $\\hat{p}$ is an unbiased estimator, but $e^{-\\hat{\\mu}}$ is positively biased. From the histograms, we also observe that $\\hat{p}$ has a larger variance than $e^{-\\hat{\\mu}}$. This translates to being typically far away from the true value versus being typically close to a value above the true value. We typically want to select the estimator with the lowest mean squared error (MSE).\n", + "\n", + "Using the true value from the dataset we can estimate the MSE value for both estimators. Because $\\hat{p}$ is unbiased, the MSE for $\\hat{p}$ will be equal to the variance, which is $\\frac{p(1-p)}{n}$. The second estimator, $e^{-\\hat{\\mu}}$, is positively biased and can be written as a function of $p$ to be $(p^{n(1-e^{-\\frac{1}{n}})}-p)^2 + (p^{n(1-e^{-\\frac{2}{n}})}-p^{2n(1-e^{-\\frac{1}{n}})})$." + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/estimation/data/911_Calls.csv b/book/estimation/data/911_Calls.csv new file mode 100644 index 0000000..cbba7e2 --- /dev/null +++ b/book/estimation/data/911_Calls.csv @@ -0,0 +1,5933 @@ +Date,Call time +1/16/2024,6:01:40 AM +1/16/2024,6:02:35 AM +1/16/2024,6:03:01 AM +1/16/2024,6:03:14 AM +1/16/2024,6:03:16 AM +1/16/2024,6:03:17 AM +1/16/2024,6:04:18 AM +1/16/2024,6:04:57 AM +1/16/2024,6:06:44 AM +1/16/2024,6:07:40 AM +1/16/2024,6:09:44 AM +1/16/2024,6:09:46 AM +1/16/2024,6:10:16 AM +1/16/2024,6:10:57 AM +1/16/2024,6:12:00 AM +1/16/2024,6:15:15 AM +1/16/2024,6:15:52 AM +1/16/2024,6:16:27 AM +1/16/2024,6:16:32 AM +1/16/2024,6:16:42 AM 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+2/14/2024,7:30:03 AM +2/14/2024,7:30:46 AM +2/14/2024,7:31:14 AM +2/14/2024,7:33:12 AM +2/14/2024,7:33:13 AM +2/14/2024,7:33:51 AM +2/14/2024,7:34:09 AM +2/14/2024,7:35:10 AM +2/14/2024,7:36:05 AM +2/14/2024,7:38:52 AM +2/14/2024,7:40:14 AM +2/14/2024,7:41:24 AM +2/14/2024,7:41:46 AM +2/14/2024,7:47:24 AM +2/14/2024,7:49:58 AM +2/14/2024,7:50:48 AM +2/14/2024,7:50:59 AM +2/14/2024,7:51:42 AM +2/14/2024,7:52:05 AM +2/14/2024,7:53:17 AM +2/14/2024,7:54:34 AM +2/14/2024,7:55:47 AM +2/14/2024,7:56:10 AM +2/14/2024,7:57:35 AM +2/14/2024,7:58:26 AM From 490119bb4bc1e24253420f8b1c80dad8f3fd0d0f Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Sun, 6 Jul 2025 11:44:34 -0400 Subject: [PATCH 05/22] add inital draft of MLE section in estimation and reformat 911 calls for consistency --- book/_toc.yml | 1 + book/estimation/911_Calls_Estimation.ipynb | 56 ++-- .../Maximum_Likelihood_Estimate.ipynb | 315 ++++++++++++++++++ 3 files changed, 336 insertions(+), 36 deletions(-) create mode 100644 book/estimation/Maximum_Likelihood_Estimate.ipynb diff --git a/book/_toc.yml b/book/_toc.yml index 0db1250..316c257 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -12,6 +12,7 @@ parts: sections: - file: estimation/Birth_Month_Estimation.ipynb - file: estimation/911_Calls_Estimation.ipynb + - file: estimation/Maximum_Likelihood_Estimate.ipynb - caption: Miscellaneous chapters: - file: references.md diff --git a/book/estimation/911_Calls_Estimation.ipynb b/book/estimation/911_Calls_Estimation.ipynb index 9327a07..5439738 100644 --- a/book/estimation/911_Calls_Estimation.ipynb +++ b/book/estimation/911_Calls_Estimation.ipynb @@ -33,7 +33,7 @@ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", - "import math\n" + "import math" ] }, { @@ -50,23 +50,18 @@ "# Read in the data and extract calls between 6 and 7\n", "df = pd.read_csv(\"data/911_Calls.csv\")\n", "\n", - "df['datetime'] = pd.to_datetime(df['Date'] + ' ' + df['Call time'])\n", - " \n", - "df['date'] = df['datetime'].dt.date.astype(str)\n", - "df['hour'] = df['datetime'].dt.hour\n", - "df['minute'] = df['datetime'].dt.minute\n", + "df[\"datetime\"] = pd.to_datetime(df[\"Date\"] + \" \" + df[\"Call time\"])\n", "\n", - "df_hour = df[df['hour'] == 6]\n", - "counts = (\n", - " df_hour\n", - " .groupby(['date', 'minute'])\n", - " .size()\n", - " .reset_index(name='count')\n", - " )\n", + "df[\"date\"] = df[\"datetime\"].dt.date.astype(str)\n", + "df[\"hour\"] = df[\"datetime\"].dt.hour\n", + "df[\"minute\"] = df[\"datetime\"].dt.minute\n", + "\n", + "df_hour = df[df[\"hour\"] == 6]\n", + "counts = df_hour.groupby([\"date\", \"minute\"]).size().reset_index(name=\"count\")\n", "\n", "hourly_counts = {}\n", - "for date, grp in counts.groupby('date'):\n", - " series = grp.set_index('minute')['count']\n", + "for date, grp in counts.groupby(\"date\"):\n", + " series = grp.set_index(\"minute\")[\"count\"]\n", " full = series.reindex(range(60), fill_value=0)\n", " hourly_counts[date] = full.tolist()" ] @@ -96,10 +91,7 @@ }, "outputs": [], "source": [ - "p_hat = [\n", - " counts.count(0) / 60.0\n", - " for counts in hourly_counts.values()\n", - "]\n", + "p_hat = [counts.count(0) / 60.0 for counts in hourly_counts.values()]\n", "\n", "p_hat" ] @@ -137,10 +129,7 @@ }, "outputs": [], "source": [ - "mle = [\n", - " math.exp(-1 * (sum(counts) / 60.0))\n", - " for counts in hourly_counts.values()\n", - "]\n", + "mle = [math.exp(-1 * (sum(counts) / 60.0)) for counts in hourly_counts.values()]\n", "\n", "mle" ] @@ -166,17 +155,12 @@ "source": [ "# compute the true parameter over the full dataset\n", "call_counts_full_dataset = (\n", - " df\n", - " .groupby(['date', 'hour', 'minute'])\n", - " .size()\n", - " .reset_index(name='count')\n", - " )\n", + " df.groupby([\"date\", \"hour\", \"minute\"]).size().reset_index(name=\"count\")\n", + ")\n", "full_counts = []\n", - "for (_, _), grp in call_counts_full_dataset.groupby(['date', 'hour']):\n", - " ser = grp.set_index('minute')['count']\n", - " full_counts.extend(\n", - " ser.reindex(range(60), fill_value=0).tolist()\n", - " )\n", + "for (_, _), grp in call_counts_full_dataset.groupby([\"date\", \"hour\"]):\n", + " ser = grp.set_index(\"minute\")[\"count\"]\n", + " full_counts.extend(ser.reindex(range(60), fill_value=0).tolist())\n", "\n", "true_parameter = full_counts.count(0) / len(full_counts)\n", "print(f\"Estimated p (full dataset) = {true_parameter}\")\n", @@ -188,14 +172,14 @@ "plt.title(\"Estimation $\\hat{p}$\")\n", "plt.ylabel(\"frequency\")\n", "\n", - "plt.axvline(x=true_parameter, color='r', linestyle='--')\n", + "plt.axvline(x=true_parameter, color=\"r\", linestyle=\"--\")\n", "\n", "plt.subplot(1, 2, 2)\n", "plt.hist(mle)\n", "plt.title(\"Estimation $e^{-\\mu}$\")\n", "plt.ylabel(\"frequency\")\n", "\n", - "plt.axvline(x=true_parameter, color='r', linestyle='--')\n", + "plt.axvline(x=true_parameter, color=\"r\", linestyle=\"--\")\n", "\n", "plt.show()" ] @@ -207,7 +191,7 @@ "source": [ "Given these two estimators, we need to determine the best one. $\\hat{p}$ is an unbiased estimator, but $e^{-\\hat{\\mu}}$ is positively biased. From the histograms, we also observe that $\\hat{p}$ has a larger variance than $e^{-\\hat{\\mu}}$. This translates to being typically far away from the true value versus being typically close to a value above the true value. We typically want to select the estimator with the lowest mean squared error (MSE).\n", "\n", - "Using the true value from the dataset we can estimate the MSE value for both estimators. Because $\\hat{p}$ is unbiased, the MSE for $\\hat{p}$ will be equal to the variance, which is $\\frac{p(1-p)}{n}$. The second estimator, $e^{-\\hat{\\mu}}$, is positively biased and can be written as a function of $p$ to be $(p^{n(1-e^{-\\frac{1}{n}})}-p)^2 + (p^{n(1-e^{-\\frac{2}{n}})}-p^{2n(1-e^{-\\frac{1}{n}})})$." + "Using the true value from the dataset we can estimate the MSE value for both estimators. Because $\\hat{p}$ is unbiased, the MSE for $\\hat{p}$ will be equal to the variance, which is $\\frac{p(1-p)}{n}$. The second estimator, $e^{-\\hat{\\mu}}$, is positively biased and can be written as a function of $p$ to be $(p^{n(1-e^{-\\frac{1}{n}})}-p)^2 + (p^{n(1-e^{-\\frac{2}{n}})}-p^{2n(1-e^{-\\frac{1}{n}})})$. Given $p=0.2108$ and $n=60$ in this scenario, the MSE is about $0.0028$ for $\\hat{p}$ and $0.0012$ for $e^{-\\hat{\\mu}}$. " ] } ], diff --git a/book/estimation/Maximum_Likelihood_Estimate.ipynb b/book/estimation/Maximum_Likelihood_Estimate.ipynb new file mode 100644 index 0000000..2403f15 --- /dev/null +++ b/book/estimation/Maximum_Likelihood_Estimate.ipynb @@ -0,0 +1,315 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a66e885f", + "metadata": {}, + "source": [ + "# Maximum Likelihood Estimation\n", + "\n", + "In this section we will be exploring maximum likelihood estimation (MLE) across various distributions. " + ] + }, + { + "cell_type": "markdown", + "id": "8e8754b1", + "metadata": {}, + "source": [ + "We will start by deriving the MLE for the exponential distribution. The parameter we want to estimate is $\\lambda$. We start with the probability density function for the exponential distribution: \n", + "\n", + "$$\n", + "f(x)=\\lambda e^{-\\lambda x}\n", + "$$\n", + "\n", + "Now we can use this to calculate the likelihood and log likelihood.\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "L(\\lambda) &= \\prod_{i=1}^n f(x_i) \\\\\n", + "&= \\prod_{i=1}^n \\lambda e^{-\\lambda x_i}\\\\\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\ln L(\\lambda) &= \\ln \\left[ \\prod_{i=1}^n \\lambda e^{-\\lambda x_i} \\right] \\\\\n", + "&= \\sum_{i=1}^n \\ln \\left[ \\lambda e^{-\\lambda x_i} \\right] \\\\\n", + "&= n \\ln \\lambda - \\lambda \\sum_{i=1}^n x_i \\\\\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "We differentiate the log-likelihood with respect to $\\lambda$.\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\frac{\\mathrm{d}}{\\mathrm{d}\\lambda} \\ln L(\\lambda) &= \\frac{\\mathrm{d}}{\\mathrm{d}\\lambda} \\left[ n \\ln \\lambda - \\lambda \\sum_{i=1}^n x_i \\right] \\\\\n", + "&= \\frac{\\mathrm{d}}{\\mathrm{d}\\lambda} n \\ln \\lambda - \\frac{\\mathrm{d}}{\\mathrm{d}\\lambda} \\lambda \\sum_{i=1}^n x_i \\\\\n", + "&= n \\frac{1}{\\lambda} - \\sum_{i=1}^n x_i \\\\\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "Now we set the result equal to $0$ to calculate $\\hat{\\lambda}$.\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "n \\frac{1}{\\lambda} - \\sum_{i=1}^n x_i &= 0 \\\\\n", + "n \\frac{1}{\\lambda} &= \\sum_{i=1}^n x_i \\\\\n", + "\\frac{1}{\\lambda} &= \\frac{\\sum_{i=1}^n x_i}{n} \\\\\n", + "\\lambda &= \\frac{n}{\\sum_{i=1}^n x_i} \\\\\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "So we arrive at $\\hat{\\lambda} = \\frac{n}{\\sum_{i=1}^n x_i}$." + ] + }, + { + "cell_type": "markdown", + "id": "61b6df4b", + "metadata": {}, + "source": [ + "Next we will derive the MLE for the normal distribution parameters $\\mu$ and $\\sigma$. We start again with the probability density function for the normal distribution.\n", + "\n", + "$$\n", + "f(x)=\\frac{1}{\\sigma \\sqrt{2 \\pi}}e^{-\\frac 1 2 (\\frac{x-\\mu}{\\sigma})^2}\n", + "$$\n", + "\n", + "Now we calculate the likelihood and log-likelihood.\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "L &= \\prod_{i=1}^n f(x_i) \\\\\n", + "&= \\prod_{i=1}^n \\frac{1}{\\sigma \\sqrt{2 \\pi}}e^{-\\frac 1 2 (\\frac{x_i-\\mu}{\\sigma})^2} \\\\\n", + "\\ln L &= \\ln \\left[ \\prod_{i=1}^n \\frac{1}{\\sigma \\sqrt{2 \\pi}}e^{-\\frac 1 2 (\\frac{x_i-\\mu}{\\sigma})^2} \\right] \\\\\n", + "&= \\sum_{i=1}^n \\ln \\left[ \\frac{1}{\\sigma \\sqrt{2 \\pi}}e^{-\\frac 1 2 (\\frac{x_i-\\mu}{\\sigma})^2} \\right] \\\\\n", + "&= -\\frac n 2 \\ln (2\\pi) - \\frac n 2 \\ln (\\sigma ^ 2) - \\frac 1 {2\\sigma ^2} \\sum_{i=1}^n (x_i - \\mu)^2\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "We differentiate the log-likelihood with respect to $\\mu$ and $\\sigma$.\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\frac{\\mathrm{d}}{\\mathrm{d}\\mu} \\ln L &= \\frac{\\mathrm{d}}{\\mathrm{d}\\mu} \\left[ -\\frac n 2 \\ln (2\\pi) - \\frac n 2 \\ln (\\sigma ^ 2) - \\frac 1 {2\\sigma ^2} \\sum_{i=1}^n (x_i - \\mu)^2 \\right] \\\\\n", + "&= \\frac 1 {\\sigma ^2} \\sum_{i=1}^n (x_i - \\mu)\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\frac{\\mathrm{d}}{\\mathrm{d}\\sigma} \\ln L &= \\frac{\\mathrm{d}}{\\mathrm{d}\\sigma} \\left[ -\\frac n 2 \\ln (2\\pi) - \\frac n 2 \\ln (\\sigma ^ 2) - \\frac 1 {2\\sigma ^2} \\sum_{i=1}^n (x_i - \\mu)^2 \\right] \\\\\n", + "&= - \\frac n \\sigma + \\frac 1 {\\sigma ^3} \\sum_{i=1}^n (x_i - \\mu)^2\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "Now we set the result to $0$ to calculate $\\hat{\\mu}$ and $\\hat{\\sigma}$.\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "0 &= \\frac 1 {\\sigma ^2} \\sum_{i=1}^n (x_i - \\mu) \\\\\n", + "0 &= \\frac 1 {\\sigma ^2} \\sum_{i=1}^n x_i - n \\mu \\\\\n", + "n \\mu &= \\sum_{i=1}^n x_i \\\\\n", + "\\mu &= \\frac 1 n \\sum_{i=1}^n x_i\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "We can drop the $\\frac 1 {\\sigma ^2}$ term, as the partial derivative is only equal to $0$ if $\\sum_{i=1}^n x_i - n \\mu = 0$.\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "0 &= - \\frac n \\sigma + \\frac 1 {\\sigma ^3} \\sum_{i=1}^n (x_i - \\mu)^2 \\\\\n", + "\\frac n \\sigma &= \\frac 1 {\\sigma ^3} \\sum_{i=1}^n (x_i - \\mu)^2\\\\\n", + "n \\sigma ^2 &= \\sum_{i=1}^n (x_i - \\mu)^2 \\\\\n", + "\\sigma &= \\sqrt{\\frac{\\sum_{i=1}^n (x_i - \\mu)^2}{n}}\n", + "\\end{aligned}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "8f8bd062", + "metadata": {}, + "source": [ + "Now that we have calculated these estimators, let's confirm that they accurately estimate where the likelihood is maximized. To do so, we can graph the likelihood function of each and the corresponding MLE." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cc6e1a6", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.widgets import Slider" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9d75c92", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "rng = np.random.default_rng(0)\n", + "data_exp = rng.exponential(scale=2.0, size=50)\n", + "\n", + "lambdas = np.linspace(0.01, 1.5, 500)\n", + "\n", + "# log likelihood for lambda\n", + "n = data_exp.size\n", + "sum_x = data_exp.sum()\n", + "logL = n * np.log(lambdas) - lambdas * sum_x\n", + "\n", + "\n", + "plt.figure()\n", + "plt.plot(lambdas, logL, lw=2)\n", + "plt.axvline(n / sum_x, color=\"C1\", ls=\"--\", label=f\"MLE $\\lambda$={n/sum_x:.2f}\")\n", + "plt.title(\"Exponential Likelihood\")\n", + "plt.xlabel(\"$\\lambda$\")\n", + "plt.ylabel(\"$L(\\lambda)$\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e1686bb", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "data_norm = rng.normal(loc=1.0, scale=2.0, size=50)\n", + "mu = np.linspace(-2, 5, 200)\n", + "\n", + "# log likelihood for mu\n", + "sigma0 = data_norm.std(ddof=0)\n", + "logL_mu = [\n", + " (-len(data_norm) * np.log(sigma0) - np.sum((data_norm - m) ** 2) / (2 * sigma0**2))\n", + " for m in mu\n", + "]\n", + "\n", + "plt.figure()\n", + "plt.plot(mu, logL_mu)\n", + "plt.axvline(\n", + " data_norm.mean(), color=\"r\", ls=\"--\", label=f\"MLE $\\mu$={data_norm.mean():.2f}\"\n", + ")\n", + "plt.title(\"Log-Likelihood vs $\\mu$\")\n", + "plt.xlabel(\"$\\mu$\")\n", + "plt.ylabel(\"$\\ln L(\\mu)$\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5f5b9a5", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "data_norm = rng.normal(loc=1.0, scale=2.0, size=50)\n", + "sigma = np.linspace(0.1, 5, 200)\n", + "\n", + "# log likelihood for sigma\n", + "mu0 = data_norm.mean()\n", + "logL_sigma = [\n", + " -len(data_norm) * np.log(s) - np.sum((data_norm - mu0) ** 2) / (2 * s**2)\n", + " for s in sigma\n", + "]\n", + "\n", + "plt.figure()\n", + "plt.plot(sigma, logL_sigma)\n", + "plt.axvline(\n", + " np.sqrt(np.sum((data_norm - mu0) ** 2) / len(data_norm)),\n", + " color=\"r\",\n", + " ls=\"--\",\n", + " label=f\"MLE $\\sigma$={np.sqrt(((data_norm-mu0)**2).mean()):.2f}\",\n", + ")\n", + "plt.title(\"Log-Likelihood vs $\\sigma$\")\n", + "plt.xlabel(\"$\\sigma$\")\n", + "plt.ylabel(\"$\\ln L(\\sigma)$\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "998164da", + "metadata": {}, + "source": [ + "Something to note when calculating MLE is that we do not consider the value of the likelihood and the consequence of this value, only the fact that it is the maximum. This analysis of the value of the likelihood is out of the scope of the course.\n", + "\n", + "Another important property possessed by the MLE is that it is functionally invariant. This property stipulates that if you have some MLE $\\hat{\\theta}$ for a parameter $\\theta$ and wish to calculate the MLE on a transformation $f(\\theta)$, the MLE for $\\alpha=f(\\theta)$ is $\\hat{\\alpha}=f(\\hat{\\theta})$. \n", + "\n", + "To illustrate this property let is consider again the exponential distribution. We would like to compute the MLE for the expectation of the exponential distribution, $\\mathbb{E}[X]$. \n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\mathbb{E}[X] &= \\frac 1 \\lambda \\\\\n", + "\\hat{ \\mathbb{E}}[X] &= \\frac 1 {\\hat{\\lambda}} \\\\\n", + "\\hat{ \\mathbb{E}}[X] &= \\frac 1 {\\frac{n}{\\sum_{i=1}^n x_i}} \\\\\n", + "\\hat{ \\mathbb{E}}[X] &= \\frac{\\sum_{i=1}^n x_i}{n} \\\\\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "In the code below we graph the likelihood function for this estimator to confirm that the maximum likelihood estimator of the expectation of the exponential distribution is equal to the reciprocal of the maximum likelihood estimator for $\\lambda$. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93522804", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "rng = np.random.default_rng(0)\n", + "data_exp = rng.exponential(scale=2.0, size=50)\n", + "\n", + "exp = np.linspace(0.01, 1.5, 500)\n", + "\n", + "# log likelihood for expectation of exponential distribution\n", + "n = data_exp.size\n", + "sum_x = data_exp.sum()\n", + "logL = -n * np.log(exp) - sum_x / exp\n", + "\n", + "\n", + "plt.figure()\n", + "plt.plot(exp, logL, lw=2)\n", + "plt.axvline(n / sum_x, color=\"C1\", ls=\"--\", label=f\"MLE $E[X]$={sum_x/n:.2f}\")\n", + "plt.title(\"Exponential Likelihood\")\n", + "plt.xlabel(\"$\\lambda$\")\n", + "plt.ylabel(\"$L(\\lambda)$\")\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From ebac74bd41af225e7e4e5364ab4b51b23fb2a2be Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Mon, 7 Jul 2025 10:34:12 -0400 Subject: [PATCH 06/22] reformat MLE, add initial draft of Bias and MSE sections --- book/_toc.yml | 2 + book/estimation/Bias.ipynb | 196 +++++++++++++++ .../Maximum_Likelihood_Estimate.ipynb | 20 +- book/estimation/Mean_Squared_Error.ipynb | 232 ++++++++++++++++++ 4 files changed, 443 insertions(+), 7 deletions(-) create mode 100644 book/estimation/Bias.ipynb create mode 100644 book/estimation/Mean_Squared_Error.ipynb diff --git a/book/_toc.yml b/book/_toc.yml index 316c257..228202b 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -13,6 +13,8 @@ parts: - file: estimation/Birth_Month_Estimation.ipynb - file: estimation/911_Calls_Estimation.ipynb - file: estimation/Maximum_Likelihood_Estimate.ipynb + - file: estimation/Bias.ipynb + - file: estimation/Mean_Squared_Error.ipynb - caption: Miscellaneous chapters: - file: references.md diff --git a/book/estimation/Bias.ipynb b/book/estimation/Bias.ipynb new file mode 100644 index 0000000..009a4d9 --- /dev/null +++ b/book/estimation/Bias.ipynb @@ -0,0 +1,196 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "069daf42", + "metadata": {}, + "source": [ + "# Bias\n", + "\n", + "In this section we will be looking at how bias plays into estimation. We will be looking at the maximum likelihood estimator (MLE) for $\\lambda$ in the exponential distribution and the MLE for the mean in the normal distribution to illustrate this concept.\n", + "\n", + "The MLE for $\\lambda$ was derived in [Maximum Likelihood Estimation](Maximum_Likelihood_Estimate.ipynb) and is $\\frac{n}{\\sum_{i=1}^n x_i}$. This is a biased estimator.\n", + "\n", + "The MLE for $\\mu$ in the normal distribution was also derived in [Maximum Likelihood Estimation](Maximum_Likelihood_Estimate.ipynb) and is $\\frac 1 n \\sum_{i=1}^n x_i$. This is an unbiased estimator.\n", + "\n", + "To illustrate the impact of bias on these estimators, we can simulate the estimates on many samples of data and visualize the results in a histogram.\n", + "\n", + "First, we simulate the exponential distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d2d3fe57", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81e92eb8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bias = 0.0075\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:23: SyntaxWarning: invalid escape sequence '\\h'\n", + "<>:23: SyntaxWarning: invalid escape sequence '\\h'\n", + "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_2020\\1442730867.py:23: SyntaxWarning: invalid escape sequence '\\h'\n", + " plt.title(\"Estimation $\\hat{\\lambda}$\")\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.default_rng(0)\n", + "lambda_true = 0.5\n", + "sample_size = 100\n", + "samples = 1000\n", + "\n", + "estimates = []\n", + "\n", + "# simulate samples\n", + "for i in range(samples):\n", + " sample = np.random.exponential(scale=1/lambda_true, size=sample_size)\n", + "\n", + " mle = sample_size / sample.sum()\n", + " estimates.append(mle)\n", + "\n", + "estimates = np.array(estimates)\n", + "mean_estimates = estimates.mean()\n", + "\n", + "print(f\"Bias = {mean_estimates - lambda_true:.4f}\")\n", + "\n", + "# plot estimates\n", + "plt.figure(figsize=(12, 8))\n", + "plt.hist(estimates)\n", + "plt.title(\"Estimation $\\hat{\\lambda}$\")\n", + "plt.ylabel(\"frequency\")\n", + "\n", + "plt.axvline(x=lambda_true, color=\"r\", linestyle=\"--\")\n", + "plt.axvline(x=mean_estimates, color=\"y\", linestyle=\"--\")\n", + "plt.legend([\"true value\", \"mean of estimates\"])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5c148cda", + "metadata": {}, + "source": [ + "Next we simulate the normal distribution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19397290", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bias = -0.0009\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:23: SyntaxWarning: invalid escape sequence '\\h'\n", + "<>:23: SyntaxWarning: invalid escape sequence '\\h'\n", + "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_2020\\2044214315.py:23: SyntaxWarning: invalid escape sequence '\\h'\n", + " plt.title(\"Estimation $\\hat{\\mu}$\")\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.default_rng(0)\n", + "mu_true = 0.0\n", + "sample_size = 100\n", + "samples = 1000\n", + "\n", + "estimates = []\n", + "\n", + "# simulate samples\n", + "for i in range(samples):\n", + " sample = rng.normal(loc=mu_true, scale=1.0, size=sample_size)\n", + "\n", + " mle = sample.sum() / sample_size\n", + " estimates.append(mle)\n", + "\n", + "estimates = np.array(estimates)\n", + "mean_estimates = estimates.mean()\n", + "\n", + "print(f\"Bias = {mean_estimates - mu_true:.4f}\")\n", + "\n", + "# plot estimates\n", + "plt.figure(figsize=(12, 8))\n", + "plt.hist(estimates)\n", + "plt.title(\"Estimation $\\hat{\\mu}$\")\n", + "plt.ylabel(\"frequency\")\n", + "\n", + "plt.axvline(x=mu_true, color=\"r\", linestyle=\"--\")\n", + "plt.axvline(x=mean_estimates, color=\"y\", linestyle=\"--\")\n", + "plt.legend([\"true value\", \"mean of estimates\"])\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "prob_stat_book", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/estimation/Maximum_Likelihood_Estimate.ipynb b/book/estimation/Maximum_Likelihood_Estimate.ipynb index 2403f15..bc8b308 100644 --- a/book/estimation/Maximum_Likelihood_Estimate.ipynb +++ b/book/estimation/Maximum_Likelihood_Estimate.ipynb @@ -144,8 +144,7 @@ "outputs": [], "source": [ "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.widgets import Slider" + "import matplotlib.pyplot as plt" ] }, { @@ -249,16 +248,23 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "7687f366", + "metadata": {}, + "source": [ + "```{note} \n", + "When calculating MLE we do not consider the value of the likelihood and the consequence of this value, only the fact that it is the maximum. This analysis of the value of the likelihood is out of the scope of the course.\n" + ] + }, { "cell_type": "markdown", "id": "998164da", "metadata": {}, "source": [ - "Something to note when calculating MLE is that we do not consider the value of the likelihood and the consequence of this value, only the fact that it is the maximum. This analysis of the value of the likelihood is out of the scope of the course.\n", - "\n", - "Another important property possessed by the MLE is that it is functionally invariant. This property stipulates that if you have some MLE $\\hat{\\theta}$ for a parameter $\\theta$ and wish to calculate the MLE on a transformation $f(\\theta)$, the MLE for $\\alpha=f(\\theta)$ is $\\hat{\\alpha}=f(\\hat{\\theta})$. \n", + "An important property possessed by the MLE is that it is functionally invariant. This property stipulates that if you have some MLE $\\hat{\\theta}$ for a parameter $\\theta$ and wish to calculate the MLE on a transformation $f(\\theta)$, the MLE for $\\alpha=f(\\theta)$ is $\\hat{\\alpha}=f(\\hat{\\theta})$. \n", "\n", - "To illustrate this property let is consider again the exponential distribution. We would like to compute the MLE for the expectation of the exponential distribution, $\\mathbb{E}[X]$. \n", + "To illustrate this property let us consider again the exponential distribution. We would like to compute the MLE for the expectation of the exponential distribution, $\\mathbb{E}[X]$. \n", "\n", "$$\n", "\\begin{aligned}\n", @@ -269,7 +275,7 @@ "\\end{aligned}\n", "$$\n", "\n", - "In the code below we graph the likelihood function for this estimator to confirm that the maximum likelihood estimator of the expectation of the exponential distribution is equal to the reciprocal of the maximum likelihood estimator for $\\lambda$. " + "In the code below we graph the likelihood function for this estimator to confirm that the MLE of the expectation of the exponential distribution is equal to the reciprocal of the MLE for $\\lambda$. " ] }, { diff --git a/book/estimation/Mean_Squared_Error.ipynb b/book/estimation/Mean_Squared_Error.ipynb new file mode 100644 index 0000000..3a8ad29 --- /dev/null +++ b/book/estimation/Mean_Squared_Error.ipynb @@ -0,0 +1,232 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7eada857", + "metadata": {}, + "source": [ + "# Mean Squared Error\n", + "\n", + "In this section we will be exploring mean squared error (MSE). We will be looking at the MSE of a simulated exponential and normal distribution. \n", + "\n", + "We will start by simulating the exponential distribution to estimate $\\lambda$ and plotting histograms of the estimates as well as the errors in the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "dc5e6613", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "13ed32cf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE = 0.0026\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:29: SyntaxWarning: invalid escape sequence '\\h'\n", + "<>:31: SyntaxWarning: invalid escape sequence '\\l'\n", + "<>:29: SyntaxWarning: invalid escape sequence '\\h'\n", + "<>:31: SyntaxWarning: invalid escape sequence '\\l'\n", + "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_2344\\3842916536.py:29: SyntaxWarning: invalid escape sequence '\\h'\n", + " plt.title(\"Estimation $\\hat{\\lambda}$\")\n", + "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_2344\\3842916536.py:31: SyntaxWarning: invalid escape sequence '\\l'\n", + " plt.xlabel(\"$\\lambda$ estimates\")\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.default_rng(0)\n", + "lambda_true = 0.5\n", + "sample_size = 100\n", + "samples = 1000\n", + "\n", + "estimates = []\n", + "errors = []\n", + "\n", + "# simulate samples\n", + "for i in range(samples):\n", + " sample = np.random.exponential(scale=1/lambda_true, size=sample_size)\n", + "\n", + " mle = sample_size / sample.sum()\n", + " estimates.append(mle)\n", + " error = mle - lambda_true\n", + " errors.append(error)\n", + "\n", + "estimates = np.array(estimates)\n", + "mean_estimates = estimates.mean()\n", + "errors = np.array(errors)\n", + "mse = np.mean(errors ** 2)\n", + "\n", + "print(f\"MSE = {mse:.4f}\")\n", + "\n", + "# plot estimates\n", + "plt.figure(figsize=(15, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(estimates)\n", + "plt.title(\"Estimation $\\hat{\\lambda}$\")\n", + "plt.ylabel(\"frequency\")\n", + "plt.xlabel(\"$\\lambda$ estimates\")\n", + "\n", + "plt.axvline(x=lambda_true, color=\"r\", linestyle=\"--\")\n", + "plt.axvline(x=mean_estimates, color=\"y\", linestyle=\"--\")\n", + "plt.legend([\"true value\", \"mean of estimates\"])\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(errors)\n", + "plt.title(\"Errors MSE\")\n", + "plt.ylabel(\"frequency\")\n", + "plt.xlabel(\"Error (estimate - true value)\")\n", + "\n", + "plt.axvline(x=0, color=\"r\", linestyle=\"--\")\n", + "plt.axvline(x=np.mean(errors), color=\"y\", linestyle=\"--\")\n", + "plt.legend([\"zero error\", \"mean error\"])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "bcdd22f3", + "metadata": {}, + "source": [ + "Next, we turn our attention to the normal distribution. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "57cb4bee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE = 0.0109\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:29: SyntaxWarning: invalid escape sequence '\\h'\n", + "<>:31: SyntaxWarning: invalid escape sequence '\\m'\n", + "<>:29: SyntaxWarning: invalid escape sequence '\\h'\n", + "<>:31: SyntaxWarning: invalid escape sequence '\\m'\n", + "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_2344\\2050513220.py:29: SyntaxWarning: invalid escape sequence '\\h'\n", + " plt.title(\"Estimation $\\hat{\\mu}$\")\n", + "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_2344\\2050513220.py:31: SyntaxWarning: invalid escape sequence '\\m'\n", + " plt.xlabel(\"$\\mu$ estimates\")\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.default_rng(0)\n", + "mu_true = 0.0\n", + "sample_size = 100\n", + "samples = 1000\n", + "\n", + "estimates = []\n", + "errors = []\n", + "\n", + "# simulate samples\n", + "for i in range(samples):\n", + " sample = rng.normal(loc=mu_true, scale=1.0, size=sample_size)\n", + "\n", + " mle = sample.sum() / sample_size\n", + " estimates.append(mle)\n", + " error = mle - mu_true\n", + " errors.append(error)\n", + "\n", + "estimates = np.array(estimates)\n", + "mean_estimates = estimates.mean()\n", + "errors = np.array(errors)\n", + "mse = np.mean(errors ** 2)\n", + "\n", + "print(f\"MSE = {mse:.4f}\")\n", + "\n", + "# plot estimates\n", + "plt.figure(figsize=(15, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(estimates)\n", + "plt.title(\"Estimation $\\hat{\\mu}$\")\n", + "plt.ylabel(\"frequency\")\n", + "plt.xlabel(\"$\\mu$ estimates\")\n", + "\n", + "plt.axvline(x=mu_true, color=\"r\", linestyle=\"--\")\n", + "plt.axvline(x=mean_estimates, color=\"y\", linestyle=\"--\")\n", + "plt.legend([\"true value\", \"mean of estimates\"])\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(errors)\n", + "plt.title(\"Errors MSE\")\n", + "plt.ylabel(\"frequency\")\n", + "plt.xlabel(\"Error (estimate - true value)\")\n", + "\n", + "plt.axvline(x=0, color=\"r\", linestyle=\"--\")\n", + "plt.axvline(x=np.mean(errors), color=\"y\", linestyle=\"--\")\n", + "plt.legend([\"zero error\", \"mean error\"])\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "prob_stat_book", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From d63c36870b1d1704beaadc4fd69f5537e2fe6360 Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Mon, 7 Jul 2025 11:40:12 -0400 Subject: [PATCH 07/22] add initial draft of confidence intervals in estimation --- book/_toc.yml | 1 + book/estimation/Confidence_Intervals.ipynb | 354 +++++++++++++++++++++ 2 files changed, 355 insertions(+) create mode 100644 book/estimation/Confidence_Intervals.ipynb diff --git a/book/_toc.yml b/book/_toc.yml index 228202b..f4a1cfd 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -15,6 +15,7 @@ parts: - file: estimation/Maximum_Likelihood_Estimate.ipynb - file: estimation/Bias.ipynb - file: estimation/Mean_Squared_Error.ipynb + - file: estimation/Confidence_Intervals.ipynb - caption: Miscellaneous chapters: - file: references.md diff --git a/book/estimation/Confidence_Intervals.ipynb b/book/estimation/Confidence_Intervals.ipynb new file mode 100644 index 0000000..dbac289 --- /dev/null +++ b/book/estimation/Confidence_Intervals.ipynb @@ -0,0 +1,354 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "99583ffe", + "metadata": {}, + "source": [ + "# Confidence Intervals\n", + "\n", + "In this section we will be exploring confidence intervals. We will start by simulating many confidence intervals from the standard normal distribution and plotting them alongside the true mean. Confidence intervals are calculated using the following formula:\n", + "\n", + "$$\n", + "\\bar{x} \\pm t \\times (\\frac{s}{\\sqrt n})\n", + "$$\n", + "\n", + "As we can observe from this formula, confidence intervals are dependent on the sample size (n), standard deviation, expectation, and confidence level. Feel free to play around with these values in the code below." + ] + }, + { + "cell_type": "markdown", + "id": "f4048013", + "metadata": {}, + "source": [ + "```{note}\n", + "In practice we only use a single confidence interval, but we can visualize the meaning of a confidence interval by simulating many at once.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d40993a1", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as stats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65477d73", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_23632\\640873109.py:47: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + " plt.legend()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_values = np.arange(1, 100)\n", + "point_estimates = []\n", + "ci_lower_bounds = []\n", + "ci_upper_bounds = []\n", + "n = 1000\n", + "standard_deviation = 1\n", + "expectation = 0\n", + "confidence_level = 0.95\n", + "\n", + "for x in x_values:\n", + " sample = np.random.normal(loc=expectation, scale=standard_deviation, size=n)\n", + "\n", + " point_estimate = np.mean(sample)\n", + " standard_deviation = np.std(sample, ddof=1)\n", + " t = stats.t.ppf(confidence_level, n - 1)\n", + " margin_of_error = t * (standard_deviation / np.sqrt(n))\n", + " ci_lower_bound = point_estimate - margin_of_error\n", + " ci_upper_bound = point_estimate + margin_of_error\n", + " point_estimates.append(point_estimate)\n", + " ci_lower_bounds.append(ci_lower_bound)\n", + " ci_upper_bounds.append(ci_upper_bound)\n", + "\n", + "point_estimates = np.array(point_estimates)\n", + "ci_lower_bounds = np.array(ci_lower_bounds)\n", + "ci_upper_bounds = np.array(ci_upper_bounds)\n", + "\n", + "lower_errors = point_estimates - ci_lower_bounds\n", + "upper_errors = ci_upper_bounds - point_estimates\n", + "\n", + "bounds = np.array([lower_errors, upper_errors])\n", + "\n", + "plt.figure(figsize=(10, 18))\n", + "\n", + "# unique color for each interval\n", + "colors = plt.cm.tab20(np.linspace(0, 1, len(x_values)))\n", + "np.random.shuffle(colors)\n", + "\n", + "for i, (x_val, point_est, lower_err, upper_err) in enumerate(\n", + " zip(x_values, point_estimates, lower_errors, upper_errors)\n", + "):\n", + " plt.errorbar(\n", + " point_est,\n", + " x_val,\n", + " xerr=[[lower_err], [upper_err]],\n", + " fmt=\"o\",\n", + " capsize=5,\n", + " color=colors[i],\n", + " alpha=0.8,\n", + " )\n", + "\n", + "# single color for all points\n", + "# plt.errorbar(point_estimates, x_values, xerr=bounds, fmt='o', capsize=5, label='Confidence Intervals')\n", + "\n", + "# plot intervals\n", + "plt.xlabel(\"Estimate\")\n", + "plt.title(\"Confidence Intervals for Standard normal Distribution\")\n", + "plt.yticks(x_values, [f\"Sample {i}\" for i in x_values])\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.axvline(x=0, color=\"r\", linestyle=\"--\", label=\"True Mean (0)\")\n", + "plt.legend()\n", + "plt.tight_layout\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c1033a1e", + "metadata": {}, + "source": [ + "As we can see from the simulated intervals, an $n\\%$ confidence interval is an indicator that about $n\\%$ of intervals would contain the true population mean. In this case, when we simulate 100 confidence intervals at $95\\%$ confidence level, we expect about $5$ of the intervals would not contain the true population mean." + ] + }, + { + "cell_type": "markdown", + "id": "271317e7", + "metadata": {}, + "source": [ + "Let's explore the impact of sample size, standard deviation, expectation, and confidence level on our confidence intervals. " + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "a09c17cb", + "metadata": {}, + "outputs": [], + "source": [ + "def build_confidence_intervals(\n", + " x_values, n, standard_deviation, expectation, confidence_level\n", + "):\n", + " point_estimates = []\n", + " ci_lower_bounds = []\n", + " ci_upper_bounds = []\n", + "\n", + " for i, (x, n_i, sd, exp, cl) in enumerate(\n", + " zip(x_values, n, standard_deviation, expectation, confidence_level)\n", + " ):\n", + " sample = np.random.normal(loc=exp, scale=sd, size=n_i)\n", + "\n", + " point_estimate = np.mean(sample)\n", + " standard_deviation = np.std(sample, ddof=1)\n", + " t = stats.t.ppf(cl, n_i - 1)\n", + " margin_of_error = t * (sd / np.sqrt(n_i))\n", + " ci_lower_bound = point_estimate - margin_of_error\n", + " ci_upper_bound = point_estimate + margin_of_error\n", + " point_estimates.append(point_estimate)\n", + " ci_lower_bounds.append(ci_lower_bound)\n", + " ci_upper_bounds.append(ci_upper_bound)\n", + "\n", + " point_estimates = np.array(point_estimates)\n", + " ci_lower_bounds = np.array(ci_lower_bounds)\n", + " ci_upper_bounds = np.array(ci_upper_bounds)\n", + "\n", + " lower_errors = point_estimates - ci_lower_bounds\n", + " upper_errors = ci_upper_bounds - point_estimates\n", + "\n", + " return (np.array([lower_errors, upper_errors]), point_estimates)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "c4568f8e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:24: SyntaxWarning: invalid escape sequence '\\s'\n", + "<>:24: SyntaxWarning: invalid escape sequence '\\s'\n", + "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_23632\\36135039.py:24: SyntaxWarning: invalid escape sequence '\\s'\n", + " plt.yticks(x_values, [f\"Sample $\\sigma$={i}\" for i in standard_deviation])\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_values = np.arange(1, 4)\n", + "\n", + "n = [1000, 1000, 1000]\n", + "standard_deviation = [1, 2, 3]\n", + "expectation = [0, 0, 0]\n", + "confidence_level = [0.95, 0.95, 0.95]\n", + "\n", + "bounds, point_estimates = build_confidence_intervals(\n", + " x_values, n, standard_deviation, expectation, confidence_level\n", + ")\n", + "\n", + "# plot intervals\n", + "plt.figure(figsize=(10, 2))\n", + "plt.errorbar(\n", + " point_estimates,\n", + " x_values,\n", + " xerr=bounds,\n", + " fmt=\"o\",\n", + " capsize=5,\n", + " label=\"Confidence Intervals\",\n", + ")\n", + "plt.xlabel(\"Estimate\")\n", + "plt.title(\"Confidence Intervals with varying Standard Deviation\")\n", + "plt.yticks(x_values, [f\"Sample $\\sigma$={i}\" for i in standard_deviation])\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.axvline(x=0, color=\"r\", linestyle=\"--\", label=\"True Mean (0)\")\n", + "plt.legend()\n", + "plt.tight_layout\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c882ea37", + "metadata": {}, + "outputs": [], + "source": [ + "n = [1000, 100, 10]\n", + "standard_deviation = [1, 1, 1]\n", + "expectation = [0, 0, 0]\n", + "confidence_level = [0.95, 0.95, 0.95]\n", + "\n", + "bounds, point_estimates = build_confidence_intervals(\n", + " x_values, n, standard_deviation, expectation, confidence_level\n", + ")\n", + "\n", + "# plot intervals\n", + "plt.figure(figsize=(10, 2))\n", + "plt.errorbar(\n", + " point_estimates,\n", + " x_values,\n", + " xerr=bounds,\n", + " fmt=\"o\",\n", + " capsize=5,\n", + " label=\"Confidence Intervals\",\n", + ")\n", + "plt.xlabel(\"Estimate\")\n", + "plt.title(\"Confidence Intervals with varying sample size\")\n", + "plt.yticks(x_values, [f\"Sample size={i}\" for i in n])\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.axvline(x=0, color=\"r\", linestyle=\"--\", label=\"True Mean (0)\")\n", + "plt.legend()\n", + "plt.tight_layout\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "4cbd378e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n = [1000, 1000, 1000]\n", + "standard_deviation = [1, 1, 1]\n", + "expectation = [0, 0, 0]\n", + "confidence_level = [0.99, 0.95, 0.8]\n", + "\n", + "bounds, point_estimates = build_confidence_intervals(\n", + " x_values, n, standard_deviation, expectation, confidence_level\n", + ")\n", + "\n", + "# plot intervals\n", + "plt.figure(figsize=(10, 2))\n", + "plt.errorbar(\n", + " point_estimates,\n", + " x_values,\n", + " xerr=bounds,\n", + " fmt=\"o\",\n", + " capsize=5,\n", + " label=\"Confidence Intervals\",\n", + ")\n", + "plt.xlabel(\"Estimate\")\n", + "plt.title(\"Confidence Intervals with varying confidence level\")\n", + "plt.yticks(x_values, [f\"Sample confidence level={i}\" for i in confidence_level])\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.axvline(x=0, color=\"r\", linestyle=\"--\", label=\"True Mean (0)\")\n", + "plt.legend()\n", + "plt.tight_layout\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "prob_stat_book", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1ee032300e72623fed3d43723f43563a1548e65d Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Wed, 9 Jul 2025 08:37:53 -0400 Subject: [PATCH 08/22] formatting fixes and bug fixes for estimation section --- book/estimation/911_Calls_Estimation.ipynb | 3 +- book/estimation/Bias.ipynb | 2 +- book/estimation/Birth_Month_Estimation.ipynb | 6 +- book/estimation/Confidence_Intervals.ipynb | 395 +++++++++++++++--- .../Maximum_Likelihood_Estimate.ipynb | 6 +- 5 files changed, 358 insertions(+), 54 deletions(-) diff --git a/book/estimation/911_Calls_Estimation.ipynb b/book/estimation/911_Calls_Estimation.ipynb index 5439738..a384330 100644 --- a/book/estimation/911_Calls_Estimation.ipynb +++ b/book/estimation/911_Calls_Estimation.ipynb @@ -191,7 +191,8 @@ "source": [ "Given these two estimators, we need to determine the best one. $\\hat{p}$ is an unbiased estimator, but $e^{-\\hat{\\mu}}$ is positively biased. From the histograms, we also observe that $\\hat{p}$ has a larger variance than $e^{-\\hat{\\mu}}$. This translates to being typically far away from the true value versus being typically close to a value above the true value. We typically want to select the estimator with the lowest mean squared error (MSE).\n", "\n", - "Using the true value from the dataset we can estimate the MSE value for both estimators. Because $\\hat{p}$ is unbiased, the MSE for $\\hat{p}$ will be equal to the variance, which is $\\frac{p(1-p)}{n}$. The second estimator, $e^{-\\hat{\\mu}}$, is positively biased and can be written as a function of $p$ to be $(p^{n(1-e^{-\\frac{1}{n}})}-p)^2 + (p^{n(1-e^{-\\frac{2}{n}})}-p^{2n(1-e^{-\\frac{1}{n}})})$. Given $p=0.2108$ and $n=60$ in this scenario, the MSE is about $0.0028$ for $\\hat{p}$ and $0.0012$ for $e^{-\\hat{\\mu}}$. " + "Using the true value from the dataset we can estimate the MSE value for both estimators. Because $\\hat{p}$ is unbiased, the MSE for $\\hat{p}$ will be equal to the variance, which is $\\frac{p(1-p)}{n}$. The second estimator, $e^{-\\hat{\\mu}}$, is positively biased and can be written as a function of $p$ to be $(p^{n(1-e^{-\\frac{1}{n}})}-p)^2 + (p^{n(1-e^{-\\frac{2}{n}})}-p^{2n(1-e^{-\\frac{1}{n}})})$. \n", + "" ] } ], diff --git a/book/estimation/Bias.ipynb b/book/estimation/Bias.ipynb index 009a4d9..293a4dd 100644 --- a/book/estimation/Bias.ipynb +++ b/book/estimation/Bias.ipynb @@ -7,7 +7,7 @@ "source": [ "# Bias\n", "\n", - "In this section we will be looking at how bias plays into estimation. We will be looking at the maximum likelihood estimator (MLE) for $\\lambda$ in the exponential distribution and the MLE for the mean in the normal distribution to illustrate this concept.\n", + "In this section we will be looking at how bias plays into estimation. We will be looking at the maximum likelihood estimator (MLE) for $\\lambda$ in the exponential distribution and the MLE for the mean $\\mu$ in the normal distribution to illustrate this concept.\n", "\n", "The MLE for $\\lambda$ was derived in [Maximum Likelihood Estimation](Maximum_Likelihood_Estimate.ipynb) and is $\\frac{n}{\\sum_{i=1}^n x_i}$. This is a biased estimator.\n", "\n", diff --git a/book/estimation/Birth_Month_Estimation.ipynb b/book/estimation/Birth_Month_Estimation.ipynb index 050f6b3..b8ff771 100644 --- a/book/estimation/Birth_Month_Estimation.ipynb +++ b/book/estimation/Birth_Month_Estimation.ipynb @@ -165,6 +165,10 @@ "df[\"birth_month\"]\n", "entire_dataset = pd.DataFrame({\"Month\": df[\"birth_month\"].values})\n", "\n", + "# sample from dataset \n", + "sample_dataset = df[\"birth_month\"].sample(n=100)\n", + "df_sample_dataset = pd.DataFrame({\"Month\": sample_dataset.values})\n", + "\n", "\n", "plt.figure(figsize=(15, 5))\n", "\n", @@ -192,7 +196,7 @@ "plt.subplot(1, 3, 3)\n", "sns.countplot(\n", " x=\"Month\",\n", - " data=df_sample1,\n", + " data=df_sample_dataset,\n", " order=months,\n", " stat=\"proportion\",\n", ")\n", diff --git a/book/estimation/Confidence_Intervals.ipynb b/book/estimation/Confidence_Intervals.ipynb index dbac289..8895494 100644 --- a/book/estimation/Confidence_Intervals.ipynb +++ b/book/estimation/Confidence_Intervals.ipynb @@ -13,7 +13,7 @@ "\\bar{x} \\pm t \\times (\\frac{s}{\\sqrt n})\n", "$$\n", "\n", - "As we can observe from this formula, confidence intervals are dependent on the sample size (n), standard deviation, expectation, and confidence level. Feel free to play around with these values in the code below." + "As we can observe from this formula, confidence intervals are dependent on the sample size ($n$), standard deviation, expectation, and confidence level. Feel free to play around with these values in the code below." ] }, { @@ -22,13 +22,13 @@ "metadata": {}, "source": [ "```{note}\n", - "In practice we only use a single confidence interval, but we can visualize the meaning of a confidence interval by simulating many at once.\n", + "In practice we only use a single confidence interval, but we can visualize the meaning of a confidence interval by simulating many at once. In the code example below, confidence intervals that do not capture the true parameter are colored red.\n", "```" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 33, "id": "d40993a1", "metadata": {}, "outputs": [], @@ -44,17 +44,9 @@ "id": "65477d73", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_23632\\640873109.py:47: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", - " plt.legend()\n" - ] - }, { "data": { - "image/png": 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", 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", 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" ] @@ -78,7 +70,7 @@ "\n", " point_estimate = np.mean(sample)\n", " standard_deviation = np.std(sample, ddof=1)\n", - " t = stats.t.ppf(confidence_level, n - 1)\n", + " t = stats.t.ppf((1 + confidence_level) / 2, n - 1)\n", " margin_of_error = t * (standard_deviation / np.sqrt(n))\n", " ci_lower_bound = point_estimate - margin_of_error\n", " ci_upper_bound = point_estimate + margin_of_error\n", @@ -97,34 +89,31 @@ "\n", "plt.figure(figsize=(10, 18))\n", "\n", - "# unique color for each interval\n", - "colors = plt.cm.tab20(np.linspace(0, 1, len(x_values)))\n", + "colors = plt.cm.viridis(np.linspace(0, 1, len(x_values)))\n", "np.random.shuffle(colors)\n", "\n", "for i, (x_val, point_est, lower_err, upper_err) in enumerate(\n", " zip(x_values, point_estimates, lower_errors, upper_errors)\n", "):\n", + " color = colors[i]\n", + " if point_est - lower_err > expectation or point_est + upper_err < expectation:\n", + " color = \"red\"\n", " plt.errorbar(\n", " point_est,\n", " x_val,\n", " xerr=[[lower_err], [upper_err]],\n", " fmt=\"o\",\n", " capsize=5,\n", - " color=colors[i],\n", + " color=color,\n", " alpha=0.8,\n", " )\n", "\n", - "# single color for all points\n", - "# plt.errorbar(point_estimates, x_values, xerr=bounds, fmt='o', capsize=5, label='Confidence Intervals')\n", - "\n", "# plot intervals\n", "plt.xlabel(\"Estimate\")\n", "plt.title(\"Confidence Intervals for Standard normal Distribution\")\n", "plt.yticks(x_values, [f\"Sample {i}\" for i in x_values])\n", - "plt.legend()\n", "plt.grid(True)\n", - "plt.axvline(x=0, color=\"r\", linestyle=\"--\", label=\"True Mean (0)\")\n", - "plt.legend()\n", + "plt.axvline(x=expectation, color=\"r\", linestyle=\"--\", label=\"True Mean (0)\")\n", "plt.tight_layout\n", "plt.show()" ] @@ -147,26 +136,22 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 47, "id": "a09c17cb", "metadata": {}, "outputs": [], "source": [ - "def build_confidence_intervals(\n", - " x_values, n, standard_deviation, expectation, confidence_level\n", - "):\n", + "def build_confidence_intervals(n, standard_deviation, expectation, confidence_level):\n", " point_estimates = []\n", " ci_lower_bounds = []\n", " ci_upper_bounds = []\n", "\n", - " for i, (x, n_i, sd, exp, cl) in enumerate(\n", - " zip(x_values, n, standard_deviation, expectation, confidence_level)\n", - " ):\n", + " for n_i, sd, exp, cl in zip(n, standard_deviation, expectation, confidence_level):\n", " sample = np.random.normal(loc=exp, scale=sd, size=n_i)\n", "\n", " point_estimate = np.mean(sample)\n", " standard_deviation = np.std(sample, ddof=1)\n", - " t = stats.t.ppf(cl, n_i - 1)\n", + " t = stats.t.ppf((1 + cl) / 2, n_i - 1)\n", " margin_of_error = t * (sd / np.sqrt(n_i))\n", " ci_lower_bound = point_estimate - margin_of_error\n", " ci_upper_bound = point_estimate + margin_of_error\n", @@ -184,9 +169,75 @@ " return (np.array([lower_errors, upper_errors]), point_estimates)" ] }, + { + "cell_type": "markdown", + "id": "5792f85c", + "metadata": {}, + "source": [ + "The sample size has an inverse relationship with the width of the confidence interval. If the sample size is larger, the interval becomes narrower. If the size is smaller, the interval becomes wider. Experiment with the values in the code below to see this relationship." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c882ea37", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_values = np.arange(1, 4)\n", + "\n", + "n = [1000, 100, 10]\n", + "standard_deviation = [1, 1, 1]\n", + "expectation = [0, 0, 0]\n", + "confidence_level = [0.95, 0.95, 0.95]\n", + "\n", + "bounds, point_estimates = build_confidence_intervals(\n", + " n, standard_deviation, expectation, confidence_level\n", + ")\n", + "\n", + "# plot intervals\n", + "plt.figure(figsize=(10, 2))\n", + "plt.errorbar(\n", + " point_estimates,\n", + " x_values,\n", + " xerr=bounds,\n", + " fmt=\"o\",\n", + " capsize=5,\n", + " label=\"Confidence Intervals\",\n", + ")\n", + "plt.xlabel(\"Estimate\")\n", + "plt.title(\"Confidence Intervals with varying sample size\")\n", + "plt.yticks(x_values, [f\"Sample size={i}\" for i in n])\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.axvline(x=0, color=\"r\", linestyle=\"--\", label=\"True Mean\")\n", + "plt.legend()\n", + "plt.tight_layout\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "46324a43", + "metadata": {}, + "source": [ + "The standard deviation has direct relationship with the width of the confidence interval. A larger standard deviation leads to a wider interval, whilst a smaller standard deviation leads to a narrower interval. Experiment with the values in the code below to see this relationship." + ] + }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "id": "c4568f8e", "metadata": {}, "outputs": [ @@ -196,13 +247,13 @@ "text": [ "<>:24: SyntaxWarning: invalid escape sequence '\\s'\n", "<>:24: SyntaxWarning: invalid escape sequence '\\s'\n", - "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_23632\\36135039.py:24: SyntaxWarning: invalid escape sequence '\\s'\n", + "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_15752\\1892220818.py:24: SyntaxWarning: invalid escape sequence '\\s'\n", " plt.yticks(x_values, [f\"Sample $\\sigma$={i}\" for i in standard_deviation])\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -220,7 +271,7 @@ "confidence_level = [0.95, 0.95, 0.95]\n", "\n", "bounds, point_estimates = build_confidence_intervals(\n", - " x_values, n, standard_deviation, expectation, confidence_level\n", + " n, standard_deviation, expectation, confidence_level\n", ")\n", "\n", "# plot intervals\n", @@ -238,26 +289,47 @@ "plt.yticks(x_values, [f\"Sample $\\sigma$={i}\" for i in standard_deviation])\n", "plt.legend()\n", "plt.grid(True)\n", - "plt.axvline(x=0, color=\"r\", linestyle=\"--\", label=\"True Mean (0)\")\n", + "plt.axvline(x=0, color=\"r\", linestyle=\"--\", label=\"True Mean\")\n", "plt.legend()\n", "plt.tight_layout\n", "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "e2172585", + "metadata": {}, + "source": [ + "The expectation shifts the entire confidence interval without impacting the width. Experiment with the values in the code below to see this relationship." + ] + }, { "cell_type": "code", - "execution_count": null, - "id": "c882ea37", + "execution_count": 50, + "id": "85e09575", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "n = [1000, 100, 10]\n", + "x_values = np.arange(1, 4)\n", + "\n", + "n = [100, 100, 100]\n", "standard_deviation = [1, 1, 1]\n", - "expectation = [0, 0, 0]\n", + "expectation = [0, 1, -1]\n", "confidence_level = [0.95, 0.95, 0.95]\n", "\n", "bounds, point_estimates = build_confidence_intervals(\n", - " x_values, n, standard_deviation, expectation, confidence_level\n", + " n, standard_deviation, expectation, confidence_level\n", ")\n", "\n", "# plot intervals\n", @@ -271,25 +343,43 @@ " label=\"Confidence Intervals\",\n", ")\n", "plt.xlabel(\"Estimate\")\n", - "plt.title(\"Confidence Intervals with varying sample size\")\n", - "plt.yticks(x_values, [f\"Sample size={i}\" for i in n])\n", + "plt.title(\"Confidence Intervals with varying expectations\")\n", + "plt.yticks(x_values, [f\"Sample expectation ={i}\" for i in expectation])\n", "plt.legend()\n", "plt.grid(True)\n", - "plt.axvline(x=0, color=\"r\", linestyle=\"--\", label=\"True Mean (0)\")\n", + "plt.axvline(x=expectation[0], color=\"r\", linestyle=\"--\", label=\"True Mean\")\n", + "plt.axvline(\n", + " x=expectation[1],\n", + " color=\"r\",\n", + " linestyle=\"--\",\n", + ")\n", + "plt.axvline(\n", + " x=expectation[2],\n", + " color=\"r\",\n", + " linestyle=\"--\",\n", + ")\n", "plt.legend()\n", "plt.tight_layout\n", "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "64511541", + "metadata": {}, + "source": [ + "The confidence level has an inverse relationship on the width of the confidence interval. A higher confidence level corresponds to a larger $t$ value, which means a wider interval. A lower confidence level corresponds to a narrower interval. Experiment with the values in the code below to see this relationship." + ] + }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "id": "4cbd378e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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" ] @@ -299,13 +389,15 @@ } ], "source": [ + "x_values = np.arange(1, 4)\n", + "\n", "n = [1000, 1000, 1000]\n", "standard_deviation = [1, 1, 1]\n", "expectation = [0, 0, 0]\n", "confidence_level = [0.99, 0.95, 0.8]\n", "\n", "bounds, point_estimates = build_confidence_intervals(\n", - " x_values, n, standard_deviation, expectation, confidence_level\n", + " n, standard_deviation, expectation, confidence_level\n", ")\n", "\n", "# plot intervals\n", @@ -323,11 +415,218 @@ "plt.yticks(x_values, [f\"Sample confidence level={i}\" for i in confidence_level])\n", "plt.legend()\n", "plt.grid(True)\n", - "plt.axvline(x=0, color=\"r\", linestyle=\"--\", label=\"True Mean (0)\")\n", + "plt.axvline(x=0, color=\"r\", linestyle=\"--\", label=\"True Mean\")\n", "plt.legend()\n", "plt.tight_layout\n", "plt.show()" ] + }, + { + "cell_type": "markdown", + "id": "feeb0e6b", + "metadata": {}, + "source": [ + "## Bonus: Naïve approximation vs Wilson Method\n", + "\n", + "The naïve approximation that is taught in class uses the following formula to calculate a confidence interval for a proportion:\n", + "\n", + "$$\n", + "\\left[ \\hat{p}-z_{\\frac \\alpha 2 }\\sqrt{\\frac{\\hat{p}(1-\\hat{p})}{n}}, \\hat{p}+z_{\\frac \\alpha 2 }\\sqrt{\\frac{\\hat{p}(1-\\hat{p})}{n}}\\right]\n", + "$$\n", + "\n", + "There is another method called the \"Wilson Method\" that can also be used to calculate an approximate confidence interval with large $n$ for a proportion. This method involves solving the following formula for $p$ to obtain upper and lower bounds:\n", + "\n", + "$$\n", + "\\left( \\frac X n - p \\right)^2 - (z_{\\frac{\\alpha}{2}})^2 \\frac{p(1-p)}{n} < 0\n", + "$$\n", + "\n", + "In both of these cases, the confidence level is only approximate, and the Wilson method achieves on average a better approximation. The code below simulates confidence intervals calculated using both methods." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "2d8fad43", + "metadata": {}, + "outputs": [], + "source": [ + "def naive_confidence_interval(x, n, confidence_level=0.95):\n", + " p_hat = x / n\n", + " alpha = 1 - confidence_level\n", + " z = stats.norm.ppf(1 - alpha / 2)\n", + " margin_of_error = z * np.sqrt((p_hat * (1 - p_hat)) / n)\n", + "\n", + " ci_lower = p_hat - margin_of_error\n", + " ci_upper = p_hat + margin_of_error\n", + "\n", + " return p_hat, ci_lower, ci_upper\n", + "\n", + "\n", + "def wilson_confidence_interval(x, n, confidence_level=0.95):\n", + " alpha = 1 - confidence_level\n", + " z = stats.norm.ppf(1 - alpha / 2)\n", + "\n", + " p_hat = x / n\n", + " z_squared = z**2\n", + "\n", + " p_tilde = (p_hat + z_squared / (2 * n)) / (1 + z_squared / n)\n", + "\n", + " denominator = 1 + z_squared / n\n", + " sqrt_term = np.sqrt((p_hat * (1 - p_hat) + z_squared / (4 * n)) / n)\n", + "\n", + " ci_lower = (p_hat + z_squared / (2 * n) - z * sqrt_term) / denominator\n", + " ci_upper = (p_hat + z_squared / (2 * n) + z * sqrt_term) / denominator\n", + "\n", + " return p_tilde, ci_lower, ci_upper" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "82e3c19b", + "metadata": {}, + "outputs": [], + "source": [ + "# calculate intervals for multiple samples\n", + "np.random.seed(80)\n", + "\n", + "num_samples = 50\n", + "n = 1000\n", + "p_true = 0.25\n", + "confidence_level = 0.95\n", + "\n", + "naive_lower = []\n", + "naive_upper = []\n", + "naive_point = []\n", + "\n", + "wilson_lower = []\n", + "wilson_upper = []\n", + "wilson_point = []\n", + "\n", + "for i in range(num_samples):\n", + " x = np.random.binomial(n=n, p=p_true)\n", + "\n", + " p_hat_naive, ci_lower_naive, ci_upper_naive = naive_confidence_interval(\n", + " x, n, confidence_level\n", + " )\n", + "\n", + " naive_point.append(p_hat_naive)\n", + " naive_lower.append(ci_lower_naive)\n", + " naive_upper.append(ci_upper_naive)\n", + "\n", + " p_tilde_wilson, ci_lower_wilson, ci_upper_wilson = wilson_confidence_interval(\n", + " x, n, confidence_level\n", + " )\n", + "\n", + " wilson_point.append(p_tilde_wilson)\n", + " wilson_lower.append(ci_lower_wilson)\n", + " wilson_upper.append(ci_upper_wilson)\n", + "\n", + "naive_point = np.array(naive_point)\n", + "naive_lower = np.array(naive_lower)\n", + "naive_upper = np.array(naive_upper)\n", + "\n", + "wilson_point = np.array(wilson_point)\n", + "wilson_lower = np.array(wilson_lower)\n", + "wilson_upper = np.array(wilson_upper)\n", + "\n", + "naive_lower_errors = naive_point - naive_lower\n", + "naive_upper_errors = naive_upper - naive_point\n", + "\n", + "wilson_lower_errors = wilson_point - wilson_lower\n", + "wilson_upper_errors = wilson_upper - wilson_point" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "8cb5cd44", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot results\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 10))\n", + "\n", + "sample_indices = np.arange(1, num_samples + 1)\n", + "colors = plt.cm.tab20(np.linspace(0, 1, num_samples))\n", + "\n", + "for i in range(num_samples):\n", + " contains_true = naive_lower[i] <= p_true <= naive_upper[i]\n", + " color = \"green\" if contains_true else \"red\"\n", + " alpha = 0.7 if contains_true else 0.9\n", + "\n", + " ax1.errorbar(\n", + " naive_point[i],\n", + " sample_indices[i],\n", + " xerr=[[naive_lower_errors[i]], [naive_upper_errors[i]]],\n", + " fmt=\"o\",\n", + " capsize=3,\n", + " color=color,\n", + " alpha=alpha,\n", + " markersize=4,\n", + " )\n", + "\n", + "ax1.axvline(\n", + " x=p_true,\n", + " color=\"blue\",\n", + " linestyle=\"--\",\n", + " linewidth=2,\n", + " label=f\"True Proportion ({p_true})\",\n", + ")\n", + "ax1.set_xlabel(\"Proportion Estimate\")\n", + "ax1.set_ylabel(\"Sample Number\")\n", + "ax1.set_title(\"Naive Method Confidence Intervals\")\n", + "ax1.grid(True, alpha=0.3)\n", + "ax1.legend()\n", + "\n", + "for i in range(num_samples):\n", + " contains_true = wilson_lower[i] <= p_true <= wilson_upper[i]\n", + " color = \"green\" if contains_true else \"red\"\n", + " alpha = 0.7 if contains_true else 0.9\n", + "\n", + " ax2.errorbar(\n", + " wilson_point[i],\n", + " sample_indices[i],\n", + " xerr=[[wilson_lower_errors[i]], [wilson_upper_errors[i]]],\n", + " fmt=\"o\",\n", + " capsize=3,\n", + " color=color,\n", + " alpha=alpha,\n", + " markersize=4,\n", + " )\n", + "\n", + "ax2.axvline(\n", + " x=p_true,\n", + " color=\"blue\",\n", + " linestyle=\"--\",\n", + " linewidth=2,\n", + " label=f\"True Proportion ({p_true})\",\n", + ")\n", + "ax2.set_xlabel(\"Proportion Estimate\")\n", + "ax2.set_ylabel(\"Sample Number\")\n", + "ax2.set_title(\"Wilson Method Confidence Intervals\")\n", + "ax2.grid(True, alpha=0.3)\n", + "ax2.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# wilson_errors = wilson_results['point_estimates'] - p_true\n", + "# naive_errors = naive_results['point_estimates'] - p_true\n", + "# print(f\"Wilson Method Mean Error: {np.mean(wilson_errors):.4f}, Standard Deviation: {np.std(wilson_errors):.4f}\")\n", + "# print(f\"Naive Method Mean Error: {np.mean(naive_errors):.4f}, Standard Deviation: {np.std(naive_errors):.4f}\")" + ] } ], "metadata": { diff --git a/book/estimation/Maximum_Likelihood_Estimate.ipynb b/book/estimation/Maximum_Likelihood_Estimate.ipynb index bc8b308..532519d 100644 --- a/book/estimation/Maximum_Likelihood_Estimate.ipynb +++ b/book/estimation/Maximum_Likelihood_Estimate.ipynb @@ -297,15 +297,15 @@ "# log likelihood for expectation of exponential distribution\n", "n = data_exp.size\n", "sum_x = data_exp.sum()\n", - "logL = -n * np.log(exp) - sum_x / exp\n", + "logL = -n * np.log(exp_values) - sum_x / exp_values\n", "\n", "\n", "plt.figure()\n", "plt.plot(exp, logL, lw=2)\n", "plt.axvline(n / sum_x, color=\"C1\", ls=\"--\", label=f\"MLE $E[X]$={sum_x/n:.2f}\")\n", "plt.title(\"Exponential Likelihood\")\n", - "plt.xlabel(\"$\\lambda$\")\n", - "plt.ylabel(\"$L(\\lambda)$\")\n", + "plt.xlabel(\"$E[X]$\")\n", + "plt.ylabel(\"$L(E[X])$\")\n", "plt.legend()\n", "plt.show()" ] From c3203e3863492539df6341a9b152a850458f03a1 Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Wed, 9 Jul 2025 11:55:38 -0400 Subject: [PATCH 09/22] remove seeds, update Wilson section in CI, add running average for bias, fix code style and errors --- book/estimation/911_Calls_Estimation.ipynb | 11 ++-- book/estimation/Bias.ipynb | 62 +++++++++++++++++-- book/estimation/Birth_Month_Estimation.ipynb | 1 - book/estimation/Confidence_Intervals.ipynb | 34 +++++----- .../Maximum_Likelihood_Estimate.ipynb | 6 +- book/estimation/Mean_Squared_Error.ipynb | 38 ++++++------ 6 files changed, 99 insertions(+), 53 deletions(-) diff --git a/book/estimation/911_Calls_Estimation.ipynb b/book/estimation/911_Calls_Estimation.ipynb index a384330..bcfa6fa 100644 --- a/book/estimation/911_Calls_Estimation.ipynb +++ b/book/estimation/911_Calls_Estimation.ipynb @@ -32,8 +32,7 @@ "source": [ "import numpy as np\n", "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import math" + "import matplotlib.pyplot as plt" ] }, { @@ -129,7 +128,7 @@ }, "outputs": [], "source": [ - "mle = [math.exp(-1 * (sum(counts) / 60.0)) for counts in hourly_counts.values()]\n", + "mle = [np.exp(-1 * (sum(counts) / 60.0)) for counts in hourly_counts.values()]\n", "\n", "mle" ] @@ -158,9 +157,9 @@ " df.groupby([\"date\", \"hour\", \"minute\"]).size().reset_index(name=\"count\")\n", ")\n", "full_counts = []\n", - "for (_, _), grp in call_counts_full_dataset.groupby([\"date\", \"hour\"]):\n", - " ser = grp.set_index(\"minute\")[\"count\"]\n", - " full_counts.extend(ser.reindex(range(60), fill_value=0).tolist())\n", + "for (_, _), group in call_counts_full_dataset.groupby([\"date\", \"hour\"]):\n", + " series = group.set_index(\"minute\")[\"count\"]\n", + " full_counts.extend(series.reindex(range(60), fill_value=0).tolist())\n", "\n", "true_parameter = full_counts.count(0) / len(full_counts)\n", "print(f\"Estimated p (full dataset) = {true_parameter}\")\n", diff --git a/book/estimation/Bias.ipynb b/book/estimation/Bias.ipynb index 293a4dd..5150166 100644 --- a/book/estimation/Bias.ipynb +++ b/book/estimation/Bias.ipynb @@ -64,17 +64,21 @@ } ], "source": [ - "rng = np.random.default_rng(0)\n", + "rng = np.random.default_rng()\n", "lambda_true = 0.5\n", "sample_size = 100\n", "samples = 1000\n", "\n", "estimates = []\n", + "running_avg_lambda = []\n", "\n", "# simulate samples\n", "for i in range(samples):\n", " sample = np.random.exponential(scale=1/lambda_true, size=sample_size)\n", - "\n", + " if i == 0:\n", + " running_avg_lambda.append(sample_size / sample.sum())\n", + " else:\n", + " running_avg_lambda.append((running_avg_lambda[-1] * i + sample_size / sample.sum())/ (i+1))\n", " mle = sample_size / sample.sum()\n", " estimates.append(mle)\n", "\n", @@ -139,17 +143,21 @@ } ], "source": [ - "rng = np.random.default_rng(0)\n", - "mu_true = 0.0\n", + "rng = np.random.default_rng()\n", + "mu_true = 4.2\n", "sample_size = 100\n", "samples = 1000\n", "\n", "estimates = []\n", + "running_avg_mu = []\n", "\n", "# simulate samples\n", "for i in range(samples):\n", " sample = rng.normal(loc=mu_true, scale=1.0, size=sample_size)\n", - "\n", + " if i == 0:\n", + " running_avg_mu.append(sample.sum() / sample_size)\n", + " else:\n", + " running_avg_mu.append((running_avg_mu[-1] * i + sample.sum() / sample_size)/ (i+1))\n", " mle = sample.sum() / sample_size\n", " estimates.append(mle)\n", "\n", @@ -170,6 +178,50 @@ "\n", "plt.show()" ] + }, + { + "cell_type": "markdown", + "id": "01b19fc6", + "metadata": {}, + "source": [ + "To further illustrate the concept of bias, let us consider a running average of our estimates. \n", + "\n", + "The code below displays a running average of the estimates calculated for $\\lambda$ in the exponential distribution and $\\mu$ in the normal distribution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ea7a6a0", + "metadata": {}, + "outputs": [], + "source": [ + "x = np.arange(1, samples + 1)\n", + "figure, axis = plt.subplots(2,1, figsize=(14, 15))\n", + "axis[0].plot(x, running_avg_lambda, label=\"running average\")\n", + "axis[0].axhline(y=lambda_true, color=\"r\", linestyle=\"--\", label=\"true value\")\n", + "axis[0].set_title(\"Running Average of Estimates for $\\lambda$\")\n", + "axis[0].legend()\n", + "axis[0].set_xlabel(\"Sample Index\")\n", + "axis[0].set_ylabel(\"Estimate Value\")\n", + "\n", + "axis[1].plot(x, running_avg_mu, label=\"running average\")\n", + "axis[1].axhline(y=mu_true, color=\"r\", linestyle=\"--\", label=\"true value\")\n", + "axis[1].set_title(\"Running Average of Estimates for $\\mu$\")\n", + "axis[1].legend()\n", + "axis[1].set_xlabel(\"Sample Index\")\n", + "axis[1].set_ylabel(\"Estimate Value\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7fa68952", + "metadata": {}, + "source": [ + "Initially the behavior of the running average is erratic, but converges in the long run. The dashed line in the plots is the true parameter. If the running average converges to a point above the dashed line, then there is positive bias. If the running average converges to a point below the dashed line, then there is negative bias. This example illustrates the positive bias in our estimator for $\\lambda$ and that the estimator for $\\mu$ is unbiased." + ] } ], "metadata": { diff --git a/book/estimation/Birth_Month_Estimation.ipynb b/book/estimation/Birth_Month_Estimation.ipynb index b8ff771..e1b9c6e 100644 --- a/book/estimation/Birth_Month_Estimation.ipynb +++ b/book/estimation/Birth_Month_Estimation.ipynb @@ -162,7 +162,6 @@ "df_naive_months = pd.DataFrame(naive_months, columns=[\"Month\"])\n", "\n", "# estimate using entire dataset\n", - "df[\"birth_month\"]\n", "entire_dataset = pd.DataFrame({\"Month\": df[\"birth_month\"].values})\n", "\n", "# sample from dataset \n", diff --git a/book/estimation/Confidence_Intervals.ipynb b/book/estimation/Confidence_Intervals.ipynb index 8895494..ba7c5e2 100644 --- a/book/estimation/Confidence_Intervals.ipynb +++ b/book/estimation/Confidence_Intervals.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 2, "id": "d40993a1", "metadata": {}, "outputs": [], @@ -40,13 +40,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "65477d73", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -62,7 +62,7 @@ "ci_upper_bounds = []\n", "n = 1000\n", "standard_deviation = 1\n", - "expectation = 0\n", + "expectation = 3\n", "confidence_level = 0.95\n", "\n", "for x in x_values:\n", @@ -136,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 5, "id": "a09c17cb", "metadata": {}, "outputs": [], @@ -185,7 +185,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -199,7 +199,7 @@ "\n", "n = [1000, 100, 10]\n", "standard_deviation = [1, 1, 1]\n", - "expectation = [0, 0, 0]\n", + "expectation = [3, 3, 3]\n", "confidence_level = [0.95, 0.95, 0.95]\n", "\n", "bounds, point_estimates = build_confidence_intervals(\n", @@ -221,7 +221,7 @@ "plt.yticks(x_values, [f\"Sample size={i}\" for i in n])\n", "plt.legend()\n", "plt.grid(True)\n", - "plt.axvline(x=0, color=\"r\", linestyle=\"--\", label=\"True Mean\")\n", + "plt.axvline(x=expectation, color=\"r\", linestyle=\"--\", label=\"True Mean\")\n", "plt.legend()\n", "plt.tight_layout\n", "plt.show()" @@ -482,17 +482,16 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "id": "82e3c19b", "metadata": {}, "outputs": [], "source": [ "# calculate intervals for multiple samples\n", - "np.random.seed(80)\n", "\n", - "num_samples = 50\n", + "num_samples = 100\n", "n = 1000\n", - "p_true = 0.25\n", + "p_true = 0.15\n", "confidence_level = 0.95\n", "\n", "naive_lower = []\n", @@ -539,7 +538,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "id": "8cb5cd44", "metadata": {}, "outputs": [ @@ -556,7 +555,7 @@ ], "source": [ "# plot results\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 10))\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 15))\n", "\n", "sample_indices = np.arange(1, num_samples + 1)\n", "colors = plt.cm.tab20(np.linspace(0, 1, num_samples))\n", @@ -620,12 +619,7 @@ "ax2.legend()\n", "\n", "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# wilson_errors = wilson_results['point_estimates'] - p_true\n", - "# naive_errors = naive_results['point_estimates'] - p_true\n", - "# print(f\"Wilson Method Mean Error: {np.mean(wilson_errors):.4f}, Standard Deviation: {np.std(wilson_errors):.4f}\")\n", - "# print(f\"Naive Method Mean Error: {np.mean(naive_errors):.4f}, Standard Deviation: {np.std(naive_errors):.4f}\")" + "plt.show()" ] } ], diff --git a/book/estimation/Maximum_Likelihood_Estimate.ipynb b/book/estimation/Maximum_Likelihood_Estimate.ipynb index 532519d..b57034f 100644 --- a/book/estimation/Maximum_Likelihood_Estimate.ipynb +++ b/book/estimation/Maximum_Likelihood_Estimate.ipynb @@ -158,7 +158,7 @@ }, "outputs": [], "source": [ - "rng = np.random.default_rng(0)\n", + "rng = np.random.default_rng()\n", "data_exp = rng.exponential(scale=2.0, size=50)\n", "\n", "lambdas = np.linspace(0.01, 1.5, 500)\n", @@ -190,6 +190,7 @@ }, "outputs": [], "source": [ + "rng = np.random.default_rng()\n", "data_norm = rng.normal(loc=1.0, scale=2.0, size=50)\n", "mu = np.linspace(-2, 5, 200)\n", "\n", @@ -223,6 +224,7 @@ }, "outputs": [], "source": [ + "rng = np.random.default_rng()\n", "data_norm = rng.normal(loc=1.0, scale=2.0, size=50)\n", "sigma = np.linspace(0.1, 5, 200)\n", "\n", @@ -289,7 +291,7 @@ }, "outputs": [], "source": [ - "rng = np.random.default_rng(0)\n", + "rng = np.random.default_rng()\n", "data_exp = rng.exponential(scale=2.0, size=50)\n", "\n", "exp = np.linspace(0.01, 1.5, 500)\n", diff --git a/book/estimation/Mean_Squared_Error.ipynb b/book/estimation/Mean_Squared_Error.ipynb index 3a8ad29..8a63d02 100644 --- a/book/estimation/Mean_Squared_Error.ipynb +++ b/book/estimation/Mean_Squared_Error.ipynb @@ -25,17 +25,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "13ed32cf", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE = 0.0026\n" - ] - }, { "name": "stderr", "output_type": "stream", @@ -44,15 +37,22 @@ "<>:31: SyntaxWarning: invalid escape sequence '\\l'\n", "<>:29: SyntaxWarning: invalid escape sequence '\\h'\n", "<>:31: SyntaxWarning: invalid escape sequence '\\l'\n", - "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_2344\\3842916536.py:29: SyntaxWarning: invalid escape sequence '\\h'\n", + "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_11728\\4002907151.py:29: SyntaxWarning: invalid escape sequence '\\h'\n", " plt.title(\"Estimation $\\hat{\\lambda}$\")\n", - "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_2344\\3842916536.py:31: SyntaxWarning: invalid escape sequence '\\l'\n", + "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_11728\\4002907151.py:31: SyntaxWarning: invalid escape sequence '\\l'\n", " plt.xlabel(\"$\\lambda$ estimates\")\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE = 0.0025\n" + ] + }, { "data": { - "image/png": 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", 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", 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" ] @@ -62,7 +62,7 @@ } ], "source": [ - "rng = np.random.default_rng(0)\n", + "rng = np.random.default_rng()\n", "lambda_true = 0.5\n", "sample_size = 100\n", "samples = 1000\n", @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "id": "57cb4bee", "metadata": {}, "outputs": [ @@ -129,7 +129,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "MSE = 0.0109\n" + "MSE = 0.0100\n" ] }, { @@ -140,15 +140,15 @@ "<>:31: SyntaxWarning: invalid escape sequence '\\m'\n", "<>:29: SyntaxWarning: invalid escape sequence '\\h'\n", "<>:31: SyntaxWarning: invalid escape sequence '\\m'\n", - "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_2344\\2050513220.py:29: SyntaxWarning: invalid escape sequence '\\h'\n", + "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_11728\\575748422.py:29: SyntaxWarning: invalid escape sequence '\\h'\n", " plt.title(\"Estimation $\\hat{\\mu}$\")\n", - "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_2344\\2050513220.py:31: SyntaxWarning: invalid escape sequence '\\m'\n", + "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_11728\\575748422.py:31: SyntaxWarning: invalid escape sequence '\\m'\n", " plt.xlabel(\"$\\mu$ estimates\")\n" ] }, { "data": { - "image/png": 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", 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" ] @@ -158,8 +158,8 @@ } ], "source": [ - "rng = np.random.default_rng(0)\n", - "mu_true = 0.0\n", + "rng = np.random.default_rng()\n", + "mu_true = 2.5\n", "sample_size = 100\n", "samples = 1000\n", "\n", From f9f8e5997905ac59a7dca90b862f4007542340f5 Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Thu, 10 Jul 2025 08:51:28 -0400 Subject: [PATCH 10/22] add sliders to confidence interval section and switch spelling to use british english --- book/estimation/Bias.ipynb | 2 +- book/estimation/Birth_Month_Estimation.ipynb | 2 +- book/estimation/Confidence_Intervals.ipynb | 224 +++++++------------ 3 files changed, 83 insertions(+), 145 deletions(-) diff --git a/book/estimation/Bias.ipynb b/book/estimation/Bias.ipynb index 5150166..161abf6 100644 --- a/book/estimation/Bias.ipynb +++ b/book/estimation/Bias.ipynb @@ -13,7 +13,7 @@ "\n", "The MLE for $\\mu$ in the normal distribution was also derived in [Maximum Likelihood Estimation](Maximum_Likelihood_Estimate.ipynb) and is $\\frac 1 n \\sum_{i=1}^n x_i$. This is an unbiased estimator.\n", "\n", - "To illustrate the impact of bias on these estimators, we can simulate the estimates on many samples of data and visualize the results in a histogram.\n", + "To illustrate the impact of bias on these estimators, we can simulate the estimates on many samples of data and visualise the results in a histogram.\n", "\n", "First, we simulate the exponential distribution." ] diff --git a/book/estimation/Birth_Month_Estimation.ipynb b/book/estimation/Birth_Month_Estimation.ipynb index e1b9c6e..4e95093 100644 --- a/book/estimation/Birth_Month_Estimation.ipynb +++ b/book/estimation/Birth_Month_Estimation.ipynb @@ -7,7 +7,7 @@ "source": [ "# Birth Months\n", "\n", - "In this chapter we will be estimating the probability mass function of birth months. We will be doing this using a dataset. The first step to analyze this data is to perform *data wrangling*, which is the process of cleaning the dataset and (when necessary) converting it to a suitable format. In this case we will be isolating the birth month and getting several data-sets of size $100$ that we can compare later." + "In this chapter we will be estimating the probability mass function of birth months. We will be doing this using a dataset. The first step to analyse this data is to perform *data wrangling*, which is the process of cleaning the dataset and (when necessary) converting it to a suitable format. In this case we will be isolating the birth month and getting several data-sets of size $100$ that we can compare later." ] }, { diff --git a/book/estimation/Confidence_Intervals.ipynb b/book/estimation/Confidence_Intervals.ipynb index ba7c5e2..c032f46 100644 --- a/book/estimation/Confidence_Intervals.ipynb +++ b/book/estimation/Confidence_Intervals.ipynb @@ -28,14 +28,31 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, + "id": "0532bd10", + "metadata": { + "tags": [ + "thebe-remove-input-init" + ] + }, + "outputs": [], + "source": [ + "import micropip\n", + "await micropip.install('ipywidgets')" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "d40993a1", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "import scipy.stats as stats" + "import scipy.stats as stats\n", + "import ipywidgets as widgets\n", + "from ipywidgets import interactive, fixed" ] }, { @@ -136,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "a09c17cb", "metadata": {}, "outputs": [], @@ -146,18 +163,17 @@ " ci_lower_bounds = []\n", " ci_upper_bounds = []\n", "\n", - " for n_i, sd, exp, cl in zip(n, standard_deviation, expectation, confidence_level):\n", - " sample = np.random.normal(loc=exp, scale=sd, size=n_i)\n", + " sample = np.random.normal(loc=expectation, scale=standard_deviation, size=n)\n", "\n", - " point_estimate = np.mean(sample)\n", - " standard_deviation = np.std(sample, ddof=1)\n", - " t = stats.t.ppf((1 + cl) / 2, n_i - 1)\n", - " margin_of_error = t * (sd / np.sqrt(n_i))\n", - " ci_lower_bound = point_estimate - margin_of_error\n", - " ci_upper_bound = point_estimate + margin_of_error\n", - " point_estimates.append(point_estimate)\n", - " ci_lower_bounds.append(ci_lower_bound)\n", - " ci_upper_bounds.append(ci_upper_bound)\n", + " point_estimate = np.mean(sample)\n", + " standard_deviation = np.std(sample, ddof=1)\n", + " t = stats.t.ppf((1 + confidence_level) / 2, n - 1)\n", + " margin_of_error = t * (standard_deviation / np.sqrt(n))\n", + " ci_lower_bound = point_estimate - margin_of_error\n", + " ci_upper_bound = point_estimate + margin_of_error\n", + " point_estimates.append(point_estimate)\n", + " ci_lower_bounds.append(ci_lower_bound)\n", + " ci_upper_bounds.append(ci_upper_bound)\n", "\n", " point_estimates = np.array(point_estimates)\n", " ci_lower_bounds = np.array(ci_lower_bounds)\n", @@ -166,7 +182,31 @@ " lower_errors = point_estimates - ci_lower_bounds\n", " upper_errors = ci_upper_bounds - point_estimates\n", "\n", - " return (np.array([lower_errors, upper_errors]), point_estimates)" + " return (np.array([lower_errors, upper_errors]), point_estimates)\n", + "\n", + "def update_plot(n=1000, standard_deviation=1, expectation=3, confidence_level=0.95, title=\"\", x_lims=[2,4]):\n", + " x_values = np.arange(1, 2)\n", + "\n", + " bounds, point_estimates = build_confidence_intervals(\n", + " n, standard_deviation, expectation, confidence_level\n", + " )\n", + " plt.figure(figsize=(10, 2))\n", + " plt.errorbar(\n", + " point_estimates,\n", + " x_values,\n", + " xerr=bounds,\n", + " fmt=\"o\",\n", + " capsize=5,\n", + " label=\"Confidence Intervals\",\n", + " )\n", + " plt.xlabel(\"Estimate\")\n", + " plt.xlim(x_lims[0],x_lims[1])\n", + " plt.title(f\"Confidence Intervals{title}\")\n", + " plt.legend()\n", + " plt.grid(True)\n", + " plt.axvline(x=expectation, color=\"r\", linestyle=\"--\", label=\"True Mean\")\n", + " plt.legend()\n", + " plt.show()" ] }, { @@ -185,7 +225,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -195,36 +235,13 @@ } ], "source": [ - "x_values = np.arange(1, 4)\n", - "\n", - "n = [1000, 100, 10]\n", - "standard_deviation = [1, 1, 1]\n", - "expectation = [3, 3, 3]\n", - "confidence_level = [0.95, 0.95, 0.95]\n", - "\n", - "bounds, point_estimates = build_confidence_intervals(\n", - " n, standard_deviation, expectation, confidence_level\n", - ")\n", + "sd = 1\n", + "exp = 3\n", + "conf_level = 0.95\n", "\n", - "# plot intervals\n", - "plt.figure(figsize=(10, 2))\n", - "plt.errorbar(\n", - " point_estimates,\n", - " x_values,\n", - " xerr=bounds,\n", - " fmt=\"o\",\n", - " capsize=5,\n", - " label=\"Confidence Intervals\",\n", - ")\n", - "plt.xlabel(\"Estimate\")\n", - "plt.title(\"Confidence Intervals with varying sample size\")\n", - "plt.yticks(x_values, [f\"Sample size={i}\" for i in n])\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.axvline(x=expectation, color=\"r\", linestyle=\"--\", label=\"True Mean\")\n", - "plt.legend()\n", - "plt.tight_layout\n", - "plt.show()" + "slider_n = widgets.IntSlider(value=500, min=10, max=1000, step=10, description='Sample Size')\n", + "interactive_plot = interactive(update_plot, n=slider_n, standard_deviation=fixed(sd), expectation=fixed(exp), confidence_level=fixed(conf_level), title=fixed(\" with varying sample size\"),x_lims=fixed([2,4]))\n", + "interactive_plot" ] }, { @@ -263,36 +280,13 @@ } ], "source": [ - "x_values = np.arange(1, 4)\n", - "\n", - "n = [1000, 1000, 1000]\n", - "standard_deviation = [1, 2, 3]\n", - "expectation = [0, 0, 0]\n", - "confidence_level = [0.95, 0.95, 0.95]\n", - "\n", - "bounds, point_estimates = build_confidence_intervals(\n", - " n, standard_deviation, expectation, confidence_level\n", - ")\n", + "n_val = 500\n", + "exp = 3\n", + "conf_level = 0.95\n", "\n", - "# plot intervals\n", - "plt.figure(figsize=(10, 2))\n", - "plt.errorbar(\n", - " point_estimates,\n", - " x_values,\n", - " xerr=bounds,\n", - " fmt=\"o\",\n", - " capsize=5,\n", - " label=\"Confidence Intervals\",\n", - ")\n", - "plt.xlabel(\"Estimate\")\n", - "plt.title(\"Confidence Intervals with varying Standard Deviation\")\n", - "plt.yticks(x_values, [f\"Sample $\\sigma$={i}\" for i in standard_deviation])\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.axvline(x=0, color=\"r\", linestyle=\"--\", label=\"True Mean\")\n", - "plt.legend()\n", - "plt.tight_layout\n", - "plt.show()" + "sd_slider = widgets.FloatSlider(value=5, min=1, max=9, step=0.1, description='Standard Deviation')\n", + "interactive_plot = interactive(update_plot, n=fixed(n_val), standard_deviation=sd_slider, expectation=fixed(exp), confidence_level=fixed(conf_level), title=fixed(\" with varying standard deviation\"), x_lims=fixed([2,4]))\n", + "interactive_plot\n" ] }, { @@ -305,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "id": "85e09575", "metadata": {}, "outputs": [ @@ -321,46 +315,13 @@ } ], "source": [ - "x_values = np.arange(1, 4)\n", - "\n", - "n = [100, 100, 100]\n", - "standard_deviation = [1, 1, 1]\n", - "expectation = [0, 1, -1]\n", - "confidence_level = [0.95, 0.95, 0.95]\n", + "n_val = 100\n", + "sd = 3\n", + "conf_level = 0.95\n", "\n", - "bounds, point_estimates = build_confidence_intervals(\n", - " n, standard_deviation, expectation, confidence_level\n", - ")\n", - "\n", - "# plot intervals\n", - "plt.figure(figsize=(10, 2))\n", - "plt.errorbar(\n", - " point_estimates,\n", - " x_values,\n", - " xerr=bounds,\n", - " fmt=\"o\",\n", - " capsize=5,\n", - " label=\"Confidence Intervals\",\n", - ")\n", - "plt.xlabel(\"Estimate\")\n", - "plt.title(\"Confidence Intervals with varying expectations\")\n", - "plt.yticks(x_values, [f\"Sample expectation ={i}\" for i in expectation])\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.axvline(x=expectation[0], color=\"r\", linestyle=\"--\", label=\"True Mean\")\n", - "plt.axvline(\n", - " x=expectation[1],\n", - " color=\"r\",\n", - " linestyle=\"--\",\n", - ")\n", - "plt.axvline(\n", - " x=expectation[2],\n", - " color=\"r\",\n", - " linestyle=\"--\",\n", - ")\n", - "plt.legend()\n", - "plt.tight_layout\n", - "plt.show()" + "exp_slider = widgets.FloatSlider(value=4, min=0, max=8, step=0.1, description='Expectation')\n", + "interactive_plot = interactive(update_plot, n=fixed(n_val), standard_deviation=fixed(sd), expectation=exp_slider, confidence_level=fixed(conf_level), title=fixed(\" with varying expectation\"), x_lims=fixed([0,8]))\n", + "interactive_plot\n" ] }, { @@ -389,36 +350,13 @@ } ], "source": [ - "x_values = np.arange(1, 4)\n", - "\n", - "n = [1000, 1000, 1000]\n", - "standard_deviation = [1, 1, 1]\n", - "expectation = [0, 0, 0]\n", - "confidence_level = [0.99, 0.95, 0.8]\n", + "n_val = 100\n", + "exp = 3\n", + "sd = 3\n", "\n", - "bounds, point_estimates = build_confidence_intervals(\n", - " n, standard_deviation, expectation, confidence_level\n", - ")\n", - "\n", - "# plot intervals\n", - "plt.figure(figsize=(10, 2))\n", - "plt.errorbar(\n", - " point_estimates,\n", - " x_values,\n", - " xerr=bounds,\n", - " fmt=\"o\",\n", - " capsize=5,\n", - " label=\"Confidence Intervals\",\n", - ")\n", - "plt.xlabel(\"Estimate\")\n", - "plt.title(\"Confidence Intervals with varying confidence level\")\n", - "plt.yticks(x_values, [f\"Sample confidence level={i}\" for i in confidence_level])\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.axvline(x=0, color=\"r\", linestyle=\"--\", label=\"True Mean\")\n", - "plt.legend()\n", - "plt.tight_layout\n", - "plt.show()" + "conf_slider = widgets.FloatSlider(value=0.82, min=0.65, max=0.99, step=0.01, description='Confidence Level')\n", + "interactive_plot = interactive(update_plot, n=fixed(n_val), standard_deviation=fixed(sd), expectation=fixed(exp), confidence_level=conf_slider, title=fixed(\" with varying confidence level\"),x_lims=fixed([2,4]))\n", + "interactive_plot\n" ] }, { From 349ba284c100aed990e7ba10a099077b0a932919 Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Thu, 10 Jul 2025 09:48:12 -0400 Subject: [PATCH 11/22] refactor code, add labels, format code --- book/estimation/Bias.ipynb | 172 ++++++---- book/estimation/Confidence_Intervals.ipynb | 296 +++++++++--------- .../Maximum_Likelihood_Estimate.ipynb | 2 +- 3 files changed, 267 insertions(+), 203 deletions(-) diff --git a/book/estimation/Bias.ipynb b/book/estimation/Bias.ipynb index 161abf6..565189e 100644 --- a/book/estimation/Bias.ipynb +++ b/book/estimation/Bias.ipynb @@ -20,13 +20,41 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "d2d3fe57", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", - "import matplotlib.pyplot as plt\n" + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0406145b", + "metadata": {}, + "outputs": [], + "source": [ + "def simulate_exponential(lambda_true=0.5, sample_size=100, samples=1000):\n", + " estimates = []\n", + " running_avg = []\n", + "\n", + " # simulate samples\n", + " for i in range(samples):\n", + " sample = np.random.exponential(scale=1 / lambda_true, size=sample_size)\n", + " if i == 0:\n", + " running_avg.append(sample_size / sample.sum())\n", + " else:\n", + " running_avg.append(\n", + " (running_avg[-1] * i + sample_size / sample.sum()) / (i + 1)\n", + " )\n", + " mle = sample_size / sample.sum()\n", + " estimates.append(mle)\n", + "\n", + " est = np.array(estimates)\n", + " mean_est = est.mean()\n", + " return est, mean_est, running_avg" ] }, { @@ -36,25 +64,25 @@ "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Bias = 0.0075\n" + "<>:28: SyntaxWarning: invalid escape sequence '\\h'\n", + "<>:28: SyntaxWarning: invalid escape sequence '\\h'\n", + "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_11968\\4119808314.py:28: SyntaxWarning: invalid escape sequence '\\h'\n", + " plt.title(\"Estimation $\\hat{\\lambda}$\")\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "<>:23: SyntaxWarning: invalid escape sequence '\\h'\n", - "<>:23: SyntaxWarning: invalid escape sequence '\\h'\n", - "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_2020\\1442730867.py:23: SyntaxWarning: invalid escape sequence '\\h'\n", - " plt.title(\"Estimation $\\hat{\\lambda}$\")\n" + "Bias = 0.0049\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -64,34 +92,20 @@ } ], "source": [ - "rng = np.random.default_rng()\n", "lambda_true = 0.5\n", "sample_size = 100\n", "samples = 1000\n", - "\n", - "estimates = []\n", - "running_avg_lambda = []\n", - "\n", - "# simulate samples\n", - "for i in range(samples):\n", - " sample = np.random.exponential(scale=1/lambda_true, size=sample_size)\n", - " if i == 0:\n", - " running_avg_lambda.append(sample_size / sample.sum())\n", - " else:\n", - " running_avg_lambda.append((running_avg_lambda[-1] * i + sample_size / sample.sum())/ (i+1))\n", - " mle = sample_size / sample.sum()\n", - " estimates.append(mle)\n", - "\n", - "estimates = np.array(estimates)\n", - "mean_estimates = estimates.mean()\n", - "\n", + "estimates, mean_estimates, running_avg_lambda = simulate_exponential(\n", + " lambda_true, sample_size, samples\n", + ")\n", "print(f\"Bias = {mean_estimates - lambda_true:.4f}\")\n", "\n", "# plot estimates\n", "plt.figure(figsize=(12, 8))\n", "plt.hist(estimates)\n", - "plt.title(\"Estimation $\\hat{\\lambda}$\")\n", + "plt.title(\"Estimation of $\\hat{\\lambda}$\")\n", "plt.ylabel(\"frequency\")\n", + "plt.xlabel(\"estimates\")\n", "\n", "plt.axvline(x=lambda_true, color=\"r\", linestyle=\"--\")\n", "plt.axvline(x=mean_estimates, color=\"y\", linestyle=\"--\")\n", @@ -108,6 +122,34 @@ "Next we simulate the normal distribution." ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "bacd6f68", + "metadata": {}, + "outputs": [], + "source": [ + "def simulate_normal(mu_true=4.2, sample_size=100, samples=1000):\n", + " estimates = []\n", + " running_avg = []\n", + "\n", + " # simulate samples\n", + " for i in range(samples):\n", + " sample = np.random.normal(loc=mu_true, scale=1.0, size=sample_size)\n", + " if i == 0:\n", + " running_avg.append(sample.sum() / sample_size)\n", + " else:\n", + " running_avg.append(\n", + " (running_avg[-1] * i + sample.sum() / sample_size) / (i + 1)\n", + " )\n", + " mle = sample.sum() / sample_size\n", + " estimates.append(mle)\n", + "\n", + " est = np.array(estimates)\n", + " mean_est = est.mean()\n", + " return est, mean_est, running_avg" + ] + }, { "cell_type": "code", "execution_count": null, @@ -118,22 +160,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "Bias = -0.0009\n" + "Bias = -0.0017\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "<>:23: SyntaxWarning: invalid escape sequence '\\h'\n", - "<>:23: SyntaxWarning: invalid escape sequence '\\h'\n", - "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_2020\\2044214315.py:23: SyntaxWarning: invalid escape sequence '\\h'\n", + "<>:29: SyntaxWarning: invalid escape sequence '\\h'\n", + "<>:29: SyntaxWarning: invalid escape sequence '\\h'\n", + "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_11968\\2360093515.py:29: SyntaxWarning: invalid escape sequence '\\h'\n", " plt.title(\"Estimation $\\hat{\\mu}$\")\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -143,34 +185,22 @@ } ], "source": [ - "rng = np.random.default_rng()\n", "mu_true = 4.2\n", "sample_size = 100\n", "samples = 1000\n", "\n", - "estimates = []\n", - "running_avg_mu = []\n", - "\n", - "# simulate samples\n", - "for i in range(samples):\n", - " sample = rng.normal(loc=mu_true, scale=1.0, size=sample_size)\n", - " if i == 0:\n", - " running_avg_mu.append(sample.sum() / sample_size)\n", - " else:\n", - " running_avg_mu.append((running_avg_mu[-1] * i + sample.sum() / sample_size)/ (i+1))\n", - " mle = sample.sum() / sample_size\n", - " estimates.append(mle)\n", - "\n", - "estimates = np.array(estimates)\n", - "mean_estimates = estimates.mean()\n", + "estimates, mean_estimates, running_avg_mu = simulate_normal(\n", + " mu_true, sample_size, samples\n", + ")\n", "\n", "print(f\"Bias = {mean_estimates - mu_true:.4f}\")\n", "\n", "# plot estimates\n", "plt.figure(figsize=(12, 8))\n", "plt.hist(estimates)\n", - "plt.title(\"Estimation $\\hat{\\mu}$\")\n", + "plt.title(\"Estimation of $\\hat{\\mu}$\")\n", "plt.ylabel(\"frequency\")\n", + "plt.xlabel(\"estimates\")\n", "\n", "plt.axvline(x=mu_true, color=\"r\", linestyle=\"--\")\n", "plt.axvline(x=mean_estimates, color=\"y\", linestyle=\"--\")\n", @@ -186,18 +216,50 @@ "source": [ "To further illustrate the concept of bias, let us consider a running average of our estimates. \n", "\n", - "The code below displays a running average of the estimates calculated for $\\lambda$ in the exponential distribution and $\\mu$ in the normal distribution." + "The code below displays a running average of the estimates calculated for $\\lambda$ in the exponential distribution and $\\mu$ in the normal distribution. Feel free to run the code multiple times to observe the variability in behavior." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "1ea7a6a0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:12: SyntaxWarning: invalid escape sequence '\\l'\n", + "<>:19: SyntaxWarning: invalid escape sequence '\\m'\n", + "<>:12: SyntaxWarning: invalid escape sequence '\\l'\n", + "<>:19: SyntaxWarning: invalid escape sequence '\\m'\n", + "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_11968\\3093412318.py:12: SyntaxWarning: invalid escape sequence '\\l'\n", + " axis[0].set_title(\"Running Average of Estimates for $\\lambda$\")\n", + "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_11968\\3093412318.py:19: SyntaxWarning: invalid escape sequence '\\m'\n", + " axis[1].set_title(\"Running Average of Estimates for $\\mu$\")\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "lambda_true = 0.5\n", + "mu_true = 4.2\n", + "sample_size = 100\n", + "samples = 1000\n", + "_, _, running_avg_lambda = simulate_exponential(lambda_true, sample_size, samples)\n", + "_, _, running_avg_mu = simulate_normal(mu_true, sample_size, samples)\n", + "\n", "x = np.arange(1, samples + 1)\n", - "figure, axis = plt.subplots(2,1, figsize=(14, 15))\n", + "figure, axis = plt.subplots(2, 1, figsize=(14, 15))\n", "axis[0].plot(x, running_avg_lambda, label=\"running average\")\n", "axis[0].axhline(y=lambda_true, color=\"r\", linestyle=\"--\", label=\"true value\")\n", "axis[0].set_title(\"Running Average of Estimates for $\\lambda$\")\n", diff --git a/book/estimation/Confidence_Intervals.ipynb b/book/estimation/Confidence_Intervals.ipynb index c032f46..fdebe0d 100644 --- a/book/estimation/Confidence_Intervals.ipynb +++ b/book/estimation/Confidence_Intervals.ipynb @@ -22,7 +22,7 @@ "metadata": {}, "source": [ "```{note}\n", - "In practice we only use a single confidence interval, but we can visualize the meaning of a confidence interval by simulating many at once. In the code example below, confidence intervals that do not capture the true parameter are colored red.\n", + "In practice we only use a single confidence interval, but we can visualise the meaning of a confidence interval by simulating many at once. In the code example below, confidence intervals that do not capture the true parameter are colored red.\n", "```" ] }, @@ -38,7 +38,8 @@ "outputs": [], "source": [ "import micropip\n", - "await micropip.install('ipywidgets')" + "\n", + "await micropip.install(\"ipywidgets\")" ] }, { @@ -57,21 +58,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "65477d73", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "x_values = np.arange(1, 100)\n", "point_estimates = []\n", @@ -184,7 +174,15 @@ "\n", " return (np.array([lower_errors, upper_errors]), point_estimates)\n", "\n", - "def update_plot(n=1000, standard_deviation=1, expectation=3, confidence_level=0.95, title=\"\", x_lims=[2,4]):\n", + "\n", + "def update_plot(\n", + " n=1000,\n", + " standard_deviation=1,\n", + " expectation=3,\n", + " confidence_level=0.95,\n", + " title=\"\",\n", + " x_lims=[2, 4],\n", + "):\n", " x_values = np.arange(1, 2)\n", "\n", " bounds, point_estimates = build_confidence_intervals(\n", @@ -200,7 +198,7 @@ " label=\"Confidence Intervals\",\n", " )\n", " plt.xlabel(\"Estimate\")\n", - " plt.xlim(x_lims[0],x_lims[1])\n", + " plt.xlim(x_lims[0], x_lims[1])\n", " plt.title(f\"Confidence Intervals{title}\")\n", " plt.legend()\n", " plt.grid(True)\n", @@ -222,25 +220,24 @@ "execution_count": null, "id": "c882ea37", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sd = 1\n", "exp = 3\n", "conf_level = 0.95\n", "\n", - "slider_n = widgets.IntSlider(value=500, min=10, max=1000, step=10, description='Sample Size')\n", - "interactive_plot = interactive(update_plot, n=slider_n, standard_deviation=fixed(sd), expectation=fixed(exp), confidence_level=fixed(conf_level), title=fixed(\" with varying sample size\"),x_lims=fixed([2,4]))\n", + "slider_n = widgets.IntSlider(\n", + " value=500, min=10, max=1000, step=10, description=\"Sample Size\"\n", + ")\n", + "interactive_plot = interactive(\n", + " update_plot,\n", + " n=slider_n,\n", + " standard_deviation=fixed(sd),\n", + " expectation=fixed(exp),\n", + " confidence_level=fixed(conf_level),\n", + " title=fixed(\" with varying sample size\"),\n", + " x_lims=fixed([2, 4]),\n", + ")\n", "interactive_plot" ] }, @@ -257,36 +254,30 @@ "execution_count": null, "id": "c4568f8e", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:24: SyntaxWarning: invalid escape sequence '\\s'\n", - "<>:24: SyntaxWarning: invalid escape sequence '\\s'\n", - "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_15752\\1892220818.py:24: SyntaxWarning: invalid escape sequence '\\s'\n", - " plt.yticks(x_values, [f\"Sample $\\sigma$={i}\" for i in standard_deviation])\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "n_val = 500\n", "exp = 3\n", "conf_level = 0.95\n", "\n", - "sd_slider = widgets.FloatSlider(value=5, min=1, max=9, step=0.1, description='Standard Deviation')\n", - "interactive_plot = interactive(update_plot, n=fixed(n_val), standard_deviation=sd_slider, expectation=fixed(exp), confidence_level=fixed(conf_level), title=fixed(\" with varying standard deviation\"), x_lims=fixed([2,4]))\n", - "interactive_plot\n" + "sd_slider = widgets.FloatSlider(\n", + " value=5,\n", + " min=1,\n", + " max=9,\n", + " step=0.1,\n", + " description=\"Standard Deviation\",\n", + " style={\"description_width\": \"120px\"},\n", + ")\n", + "interactive_plot = interactive(\n", + " update_plot,\n", + " n=fixed(n_val),\n", + " standard_deviation=sd_slider,\n", + " expectation=fixed(exp),\n", + " confidence_level=fixed(conf_level),\n", + " title=fixed(\" with varying standard deviation\"),\n", + " x_lims=fixed([2, 4]),\n", + ")\n", + "interactive_plot" ] }, { @@ -302,26 +293,25 @@ "execution_count": null, "id": "85e09575", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "n_val = 100\n", "sd = 3\n", "conf_level = 0.95\n", "\n", - "exp_slider = widgets.FloatSlider(value=4, min=0, max=8, step=0.1, description='Expectation')\n", - "interactive_plot = interactive(update_plot, n=fixed(n_val), standard_deviation=fixed(sd), expectation=exp_slider, confidence_level=fixed(conf_level), title=fixed(\" with varying expectation\"), x_lims=fixed([0,8]))\n", - "interactive_plot\n" + "exp_slider = widgets.FloatSlider(\n", + " value=4, min=0, max=8, step=0.1, description=\"Expectation\"\n", + ")\n", + "interactive_plot = interactive(\n", + " update_plot,\n", + " n=fixed(n_val),\n", + " standard_deviation=fixed(sd),\n", + " expectation=exp_slider,\n", + " confidence_level=fixed(conf_level),\n", + " title=fixed(\" with varying expectation\"),\n", + " x_lims=fixed([0, 8]),\n", + ")\n", + "interactive_plot" ] }, { @@ -337,26 +327,30 @@ "execution_count": null, "id": "4cbd378e", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "n_val = 100\n", "exp = 3\n", "sd = 3\n", "\n", - "conf_slider = widgets.FloatSlider(value=0.82, min=0.65, max=0.99, step=0.01, description='Confidence Level')\n", - "interactive_plot = interactive(update_plot, n=fixed(n_val), standard_deviation=fixed(sd), expectation=fixed(exp), confidence_level=conf_slider, title=fixed(\" with varying confidence level\"),x_lims=fixed([2,4]))\n", - "interactive_plot\n" + "conf_slider = widgets.FloatSlider(\n", + " value=0.82,\n", + " min=0.65,\n", + " max=0.99,\n", + " step=0.01,\n", + " description=\"Confidence Level\",\n", + " style={\"description_width\": \"110px\"},\n", + ")\n", + "interactive_plot = interactive(\n", + " update_plot,\n", + " n=fixed(n_val),\n", + " standard_deviation=fixed(sd),\n", + " expectation=fixed(exp),\n", + " confidence_level=conf_slider,\n", + " title=fixed(\" with varying confidence level\"),\n", + " x_lims=fixed([2, 4]),\n", + ")\n", + "interactive_plot" ] }, { @@ -383,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "id": "2d8fad43", "metadata": {}, "outputs": [], @@ -397,9 +391,16 @@ " ci_lower = p_hat - margin_of_error\n", " ci_upper = p_hat + margin_of_error\n", "\n", - " return p_hat, ci_lower, ci_upper\n", - "\n", - "\n", + " return p_hat, ci_lower, ci_upper" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3be3728", + "metadata": {}, + "outputs": [], + "source": [ "def wilson_confidence_interval(x, n, confidence_level=0.95):\n", " alpha = 1 - confidence_level\n", " z = stats.norm.ppf(1 - alpha / 2)\n", @@ -426,73 +427,74 @@ "outputs": [], "source": [ "# calculate intervals for multiple samples\n", - "\n", + "def build_wilson_naive_intervals(num_samples, n, p_true, confidence_level):\n", + " naive_lower = []\n", + " naive_upper = []\n", + " naive_point = []\n", + "\n", + " wilson_lower = []\n", + " wilson_upper = []\n", + " wilson_point = []\n", + "\n", + " for i in range(num_samples):\n", + " x = np.random.binomial(n=n, p=p_true)\n", + "\n", + " p_hat_naive, ci_lower_naive, ci_upper_naive = naive_confidence_interval(\n", + " x, n, confidence_level\n", + " )\n", + "\n", + " naive_point.append(p_hat_naive)\n", + " naive_lower.append(ci_lower_naive)\n", + " naive_upper.append(ci_upper_naive)\n", + "\n", + " p_tilde_wilson, ci_lower_wilson, ci_upper_wilson = wilson_confidence_interval(\n", + " x, n, confidence_level\n", + " )\n", + "\n", + " wilson_point.append(p_tilde_wilson)\n", + " wilson_lower.append(ci_lower_wilson)\n", + " wilson_upper.append(ci_upper_wilson)\n", + "\n", + " naive_point = np.array(naive_point)\n", + " naive_lower = np.array(naive_lower)\n", + " naive_upper = np.array(naive_upper)\n", + "\n", + " wilson_point = np.array(wilson_point)\n", + " wilson_lower = np.array(wilson_lower)\n", + " wilson_upper = np.array(wilson_upper)\n", + " return (\n", + " naive_point,\n", + " naive_lower,\n", + " naive_upper,\n", + " wilson_point,\n", + " wilson_lower,\n", + " wilson_upper,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8cb5cd44", + "metadata": {}, + "outputs": [], + "source": [ + "# plot results\n", "num_samples = 100\n", "n = 1000\n", "p_true = 0.15\n", "confidence_level = 0.95\n", "\n", - "naive_lower = []\n", - "naive_upper = []\n", - "naive_point = []\n", - "\n", - "wilson_lower = []\n", - "wilson_upper = []\n", - "wilson_point = []\n", - "\n", - "for i in range(num_samples):\n", - " x = np.random.binomial(n=n, p=p_true)\n", - "\n", - " p_hat_naive, ci_lower_naive, ci_upper_naive = naive_confidence_interval(\n", - " x, n, confidence_level\n", - " )\n", - "\n", - " naive_point.append(p_hat_naive)\n", - " naive_lower.append(ci_lower_naive)\n", - " naive_upper.append(ci_upper_naive)\n", - "\n", - " p_tilde_wilson, ci_lower_wilson, ci_upper_wilson = wilson_confidence_interval(\n", - " x, n, confidence_level\n", - " )\n", - "\n", - " wilson_point.append(p_tilde_wilson)\n", - " wilson_lower.append(ci_lower_wilson)\n", - " wilson_upper.append(ci_upper_wilson)\n", - "\n", - "naive_point = np.array(naive_point)\n", - "naive_lower = np.array(naive_lower)\n", - "naive_upper = np.array(naive_upper)\n", - "\n", - "wilson_point = np.array(wilson_point)\n", - "wilson_lower = np.array(wilson_lower)\n", - "wilson_upper = np.array(wilson_upper)\n", + "naive_point, naive_lower, naive_upper, wilson_point, wilson_lower, wilson_upper = (\n", + " build_wilson_naive_intervals(num_samples, n, p_true, confidence_level)\n", + ")\n", "\n", "naive_lower_errors = naive_point - naive_lower\n", "naive_upper_errors = naive_upper - naive_point\n", "\n", "wilson_lower_errors = wilson_point - wilson_lower\n", - "wilson_upper_errors = wilson_upper - wilson_point" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8cb5cd44", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot results\n", + "wilson_upper_errors = wilson_upper - wilson_point\n", + "\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 15))\n", "\n", "sample_indices = np.arange(1, num_samples + 1)\n", diff --git a/book/estimation/Maximum_Likelihood_Estimate.ipynb b/book/estimation/Maximum_Likelihood_Estimate.ipynb index b57034f..d1516bc 100644 --- a/book/estimation/Maximum_Likelihood_Estimate.ipynb +++ b/book/estimation/Maximum_Likelihood_Estimate.ipynb @@ -299,7 +299,7 @@ "# log likelihood for expectation of exponential distribution\n", "n = data_exp.size\n", "sum_x = data_exp.sum()\n", - "logL = -n * np.log(exp_values) - sum_x / exp_values\n", + "logL = -n * np.log(exp) - sum_x / exp\n", "\n", "\n", "plt.figure()\n", From 03a05c9f9ee816e016be647b310fc42a6787073d Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Thu, 10 Jul 2025 10:16:16 -0400 Subject: [PATCH 12/22] add titles to CI plots --- book/estimation/Confidence_Intervals.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/book/estimation/Confidence_Intervals.ipynb b/book/estimation/Confidence_Intervals.ipynb index fdebe0d..2ba470f 100644 --- a/book/estimation/Confidence_Intervals.ipynb +++ b/book/estimation/Confidence_Intervals.ipynb @@ -525,7 +525,7 @@ ")\n", "ax1.set_xlabel(\"Proportion Estimate\")\n", "ax1.set_ylabel(\"Sample Number\")\n", - "ax1.set_title(\"Naive Method Confidence Intervals\")\n", + "ax1.set_title(f\"Naive Method Confidence Intervals CL={confidence_level:.2f}\")\n", "ax1.grid(True, alpha=0.3)\n", "ax1.legend()\n", "\n", @@ -554,7 +554,7 @@ ")\n", "ax2.set_xlabel(\"Proportion Estimate\")\n", "ax2.set_ylabel(\"Sample Number\")\n", - "ax2.set_title(\"Wilson Method Confidence Intervals\")\n", + "ax2.set_title(f\"Wilson Method Confidence Intervals CL={confidence_level:.2f}\")\n", "ax2.grid(True, alpha=0.3)\n", "ax2.legend()\n", "\n", From cae842b7d3b1028a5856e7cf6c71056d96562bdb Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Fri, 11 Jul 2025 09:33:42 -0400 Subject: [PATCH 13/22] fix spelling, fix code issues, update wilson formula, fix plot titles in CI section --- book/estimation/Bias.ipynb | 91 ++-------------------- book/estimation/Confidence_Intervals.ipynb | 35 ++++----- 2 files changed, 18 insertions(+), 108 deletions(-) diff --git a/book/estimation/Bias.ipynb b/book/estimation/Bias.ipynb index 565189e..875c349 100644 --- a/book/estimation/Bias.ipynb +++ b/book/estimation/Bias.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "d2d3fe57", "metadata": {}, "outputs": [], @@ -62,35 +62,7 @@ "execution_count": null, "id": "81e92eb8", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:28: SyntaxWarning: invalid escape sequence '\\h'\n", - "<>:28: SyntaxWarning: invalid escape sequence '\\h'\n", - "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_11968\\4119808314.py:28: SyntaxWarning: invalid escape sequence '\\h'\n", - " plt.title(\"Estimation $\\hat{\\lambda}$\")\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bias = 0.0049\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "lambda_true = 0.5\n", "sample_size = 100\n", @@ -155,35 +127,7 @@ "execution_count": null, "id": "19397290", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bias = -0.0017\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:29: SyntaxWarning: invalid escape sequence '\\h'\n", - "<>:29: SyntaxWarning: invalid escape sequence '\\h'\n", - "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_11968\\2360093515.py:29: SyntaxWarning: invalid escape sequence '\\h'\n", - " plt.title(\"Estimation $\\hat{\\mu}$\")\n" - ] - }, - { - "data": { - "image/png": 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6dEhHz/1qwDk5JSk5OVlly5b15fy9Ro0aaceOHapRo0ahc6LwWJ8GAAAAACUgISFBX3/9tfbv36+jR4/6ZsvP5+abb9aGDRs0d+5c7d69W2PGjMlT4iMiIjRy5EgNHz5cc+bM0d69e7V582a98sormjNnzgVz9OrVSwsXLtS///1v3X///Xmeq1GjhubNm6edO3fq66+/Vq9evRQWFlbga9WoUUPx8fEaO3asvv/+eyUlJenFF1/Mc8yTTz6pL7/8UoMGDdKWLVu0e/duffjhhxo8ePAFc6JwKO0AAMAv3O7TWrMmWmvWRMudnipFR3sf3AIIwGVq5MiRcjqdqlu3rqKjoy/4PfEOHTro6aef1hNPPKGmTZvq5MmT6tOnT55j/v73v+uZZ57RhAkTVKdOHXXo0EH//e9/Va1atQvmuOeee3Ts2DGdOXMm323l3nrrLZ04cUINGzZU7969NWTIEFWsWLHA13K5XFqwYIG+++47XXPNNXruuef07LPP5jmmQYMGWrlypXbv3q2bbrpJDRs21NNPP61KlSpdMCcKxzDP/SLFFSg9PV1RUVFKS0tTZGSk1XEAALgsuN2ntWqV90rPNzX6Wc6oX5dnnjpVYktRAVx+MjMztW/fPlWrVo2rjiPgXeh8LmwPZaYdAAAAAACborQDAAAAAGBTlHYAAAAAAGyK0g4AAAAAgE1R2gEAAAAAsKkgqwMAAHApJYxKsjrCZWf/xE4FPONQREQT79AZJDX5dexgzgAAgMKitAMAAL9wOsPUuPH633asX1/wwQAA4Lz4VTcAAAAAADZFaQcAAAAAwKYo7QAAwC/c7jP68ssEffllgtwnj0oJCd7HmTNWRwMAFNLMmTMVHx8vh8OhqVOnXrL3NQxDH3zwwSV7PzujtAMAAD8xlZV1QFlZByTTIx044H2YptXBAACFkJ6erscee0xPPvmkDh8+rIcffrjE32Ps2LG69tpr8+1PTk7WbbfdVuLvd65A+OUAF6IDAAAAAORz8OBB5eTkqFOnTqpUqdIlfe/Y2NhL+n52xkw7AAAAAJSA1q1ba/DgwRo2bJjKli2rmJgYzZw5U6dPn9YDDzygiIgIXXXVVfr444/z/Ny3336rjh07qnTp0oqJiVHv3r119OhR3/OffPKJWrRooTJlyqh8+fLq3Lmz9u7d63t+//79MgxDixcvVps2bRQeHq5rrrlGX3755QXzHjx4UHfccYdKly6tyMhIde/eXT///LMkafbs2apfv74kqXr16jIMQ/v37z/v6xw+fFg9evRQ2bJlVb58ed1xxx15jl2xYoWuu+46lSpVSmXKlNGNN96oAwcOaPbs2Ro3bpy++eYbGYYhwzA0e/ZsSXlnwM9+vn/961+66aabFBYWpqZNm+r777/X+vXr1aRJE5UuXVq33nqrjhw54nvf9evXq3379qpQoYKioqLUqlUrbdq0yfd8QkKCJKlr164yDMO3LUn//e9/1bhxY4WGhqp69eoaN26ccnNzfc+PHTtWVapUUUhIiOLi4jRkyJAL/llfDEo7AAAAgIDgdp++wCOzCMdm/OGxxTVnzhxVqFBB69at0+DBg/XII4/onnvu0Q033KBNmzapQ4cO6t27t878en2P5ORktWrVStdee602bNigTz75RD///LO6d+/ue83Tp09rxIgRWr9+vZYtWyaHw6GuXbvK4/Hkee+//OUvGjlypLZs2aJatWrpvvvuy1M0f880Td155506fvy4Vq5cqaVLl2rv3r3q0aOHJKlHjx767LPPJEnr1q1TcnKy4uPj873OmTNn1KZNG5UuXVpffPGFVq9e7SvQ2dnZys3N1Z133qlWrVpp69at+vLLL/Xwww/LMAz16NFDjz/+uOrVq6fk5GQlJyf73v98xowZo7/+9a/atGmTgoKCdN999+mJJ57QSy+9pFWrVmnv3r165plnfMefPHlSffv21apVq/TVV1+pZs2a6tixo06ePCnJW+oladasWUpOTvZtf/rpp7r//vs1ZMgQffvtt5oxY4Zmz56tf/zjH5KkRYsWacqUKZoxY4Z2796tDz74wPcLDn9geTwAAACAgLBqVekCnytXrqMaNEjyba9ZU1Eez/kvfBkV1UoNG67wbX/1VYJyco7mOaZ16+Jdf+Oaa67RX//6V0nS6NGjNXHiRFWoUEEPPfSQJOmZZ57R9OnTtXXrVjVr1kzTp09Xo0aNNH78eN9rvPXWW4qPj9f333+vWrVqqVu3bnne480331TFihX17bffKjEx0bd/5MiR6tSpkyRp3Lhxqlevnvbs2aPatWvny/nZZ59p69at2rdvn6+Mz5s3T/Xq1dP69evVtGlTlS9fXpIUHR1d4HL1hQsXyuFw6I033pBhGJK8JbhMmTJasWKFmjRporS0NHXu3FlXXXWVJKlOnTq+ny9durSCgoIKtRx+5MiR6tChgyRp6NChuu+++7Rs2TLdeOONkqQBAwb4Zuol6eabb87z8zNmzFDZsmW1cuVKde7cWdHR0ZKkMmXK5Hn/f/zjHxo1apT69u0rybvS4O9//7ueeOIJjRkzRgcPHlRsbKzatWsnl8ulKlWq6LrrrvvD/MXFTDsAAAAAlJAGDRr4xk6nU+XLl88zCxsTEyNJSk1NlSRt3LhRn3/+uUqXLu17nC3ZZ5fA7927Vz179lT16tUVGRmpatWqSfIuby/ovc9+B/3s+5xr586dio+PzzN7XrduXZUpU0Y7d+4s9OfduHGj9uzZo4iICF/+cuXKKTMzU3v37lW5cuXUr18/dejQQV26dNFLL72k5OTkQr9+QZ/v7J/juX+2v/+8qamp+vOf/6xatWopKipKUVFROnXqVL4/t/N9pr/97W95/ps89NBDSk5O1pkzZ3TPPfcoIyND1atX10MPPaT333+/wBUNJYGZdgAA4CeGwsPr/jp0SHXPjg3rIgEIaDfddOoCzzrzbN144/nLqlfeuctmzfYXO9O5XC5Xnm3DMPLsOzsbfXZpu8fjUZcuXfTcc8/le62zxbtLly6Kj4/X66+/rri4OHk8HiUmJio7O7vA9z73fc5lmqbvmMLsL4jH41Hjxo319ttv53vu7Ez2rFmzNGTIEH3yySd699139de//lVLly5Vs2bNCv0+0vk/37n7fv95+/XrpyNHjmjq1KmqWrWqQkJC1Lx583x/buf7TOPGjdNdd92V77nQ0FDFx8dr165dWrp0qT777DM9+uijev7557Vy5cp8//1LAqUdAAD4hdMZruuu2/Hbjh07Cj4YAArB6Sxl+bElrVGjRnrvvfeUkJCgoKD89ezYsWPauXOnZsyYoZtuukmStHr16ot+37p16+rgwYM6dOiQb7b922+/VVpaWp7l64XJ/+6776pixYqKjIws8LiGDRuqYcOGGj16tJo3b6533nlHzZo1U3BwsNxu90V/nvNZtWqVXn31VXXs2FGSdOjQoTwX+JO8pf/c92/UqJF27dqlGjVqFPjaYWFhuv3223X77bdr0KBBql27trZt26ZGjRqV+OdgeTwAAAAAWGTQoEE6fvy47rvvPq1bt04//PCDlixZov79+8vtdvuuyD5z5kzt2bNHy5cv14gRIy76fdu1a6cGDRqoV69e2rRpk9atW6c+ffqoVatWatKkSaFfp1evXqpQoYLuuOMOrVq1Svv27dPKlSs1dOhQ/fjjj9q3b59Gjx6tL7/8UgcOHNCSJUv0/fff+34xkJCQoH379mnLli06evSosrKyLvqznVWjRg3NmzdPO3fu1Ndff61evXopLCwszzEJCQlatmyZUlJSdOLECUne6w7MnTtXY8eO1Y4dO7Rz507fCgHJe2X9N998U9u3b9cPP/ygefPmKSwsTFWrVi2x7L9HaQcAAAAAi8TFxWnNmjVyu93q0KGDEhMTNXToUEVFRcnhcMjhcGjhwoXauHGjEhMTNXz4cD3//PMX/b5nb6lWtmxZtWzZUu3atVP16tX17rvvFul1wsPD9cUXX6hKlSq66667VKdOHfXv318ZGRmKjIxUeHi4vvvuO3Xr1k21atXSww8/rMcee0wDBw6UJHXr1k233nqr2rRpo+joaC1YsOCiP9tZb731lk6cOKGGDRuqd+/eGjJkiCpWrJjnmBdffFFLly5VfHy8GjZsKEnq0KGD/ve//2np0qVq2rSpmjVrpsmTJ/tKeZkyZfT666/rxhtvVIMGDbRs2TL997//9V24r6QZpmkW77KIl5H09HRFRUUpLS3tgks6AACBL2FU0h8fhCLZP7HTefe73We0cWNTSVLjOivlbNbK+8T69VJ4+KWKByDAZGZmat++fapWrZpCQ0OtjgNclAudz4XtoXynHQAA+ImpM2e+/XXokb49O77i5wsAACg0lscDAAAAAGBTlHYAAAAAAGyK0g4AAAAAgE1R2gEAAADYDtfLxuWgJM5jSjsAAAAA23C5XJKkM2fOWJwEuHhnz+Oz53VxcPV4AADgJ4ZCQqr+OnRIVc+ODesiAbA9p9OpMmXKKDU1VZL3PuAG/99AgDFNU2fOnFFqaqrKlCkjp9NZ7NeitAMAAL9wOsPVvPn+33bs31/QoQCQR2xsrCT5ijsQqMqUKeM7n4uL0g4AAADAVgzDUKVKlVSxYkXl5ORYHQcoFpfLdVEz7GdR2gEAAADYktPpLJHSAwQyLkQHAAD8wu3O0MaNTbVxY1O5Tx+Xmjb1PjIyrI4GAEDAYKYdAAD4iUcnT27wDt250oZfxx6PdZEAAAgwzLQDAAAAAGBTlHYAAAAAAGyK0g4AAAAAgE1R2gEAAAAAsClKOwAAAAAANsXV4wEAgN+4XBV+26hQoeADAQDAeVHaAQCAXzidpXTjjUd+23HkSMEHAwCA82J5PAAAAAAANkVpBwAAAADApijtAADAL9zuDG3e3FqbN7eW+/RxqXVr7yMjw+poAAAEDL7TDgAA/MSjtLSV3qE7V1r569jjsS4SAAABhpl2AAAAAABsitIOAAAAAIBNUdoBAAAAALApSjsAAAAAADZFaQcAAAAAwKa4ejwAAPAbhyP8t43w8IIPBAAA50VpBwAAfuF0llLLlqd/23H6dMEHAwCA82J5PAAAAAAANkVpBwAAAADApijtAADAL9zuTG3d2klbt3aS+8wvUqdO3kdmptXRAAAIGHynHQAA+Ilbx49/5B3mZksf/Tp2u62LBABAgGGmHQAAAAAAm6K0AwAAAABgU5R2AAAAAABsitIOAAAAAIBNUdoBAAAAALApSjsAAAAAADbFLd8AAIBfOJ2l1Lq1+dsO0yz4YAAAcF7MtAMAAAAAYFOUdgAAAAAAbIrSDgAA/MLtztSOHfdox4575D7zi3TPPd5HZqbV0QAACBh8px0AAFyUhFFJ590f7MzUzPaLJEk9Z9+pbxZ5x3Xi71VGcOglyxdo9k/sZHUEAICNMNMOAAAAAIBNUdoBAAAAALApSjsAAAAAADZFaQcAAAAAwKYo7QAAAAAA2BSlHQAAAAAAm+KWbwAAwC+y3SF6eKn3Nm/ZjmDVGe4dZ7hCrIwFAEBAobQDAAA/MZTtDj075N7sAAAUA8vjAQAAAACwKWbaAQCAXwQZOeqXOE2S9M43A/W3j2dIkp7q8Jiyg1xWRgMAIGAw0w4AAPzC4XCrxZ+WqcWflsllZuvu7ct09/ZlcnrcVkcDACBgUNoBAAAAALApSjsAAAAAADZFaQcAAAAAwKYo7QAAAAAA2BSlHQAAAAAAm6K0AwAAAABgU5aW9gkTJqhp06aKiIhQxYoVdeedd2rXrl15jjFNU2PHjlVcXJzCwsLUunVr7dixI88xWVlZGjx4sCpUqKBSpUrp9ttv148//ngpPwoAADhHtjtEg5e9rcHL3laaI1KNBr+tRoPfVoYrxOpoAAAEDEtL+8qVKzVo0CB99dVXWrp0qXJzc3XLLbfo9OnTvmMmTZqkyZMna9q0aVq/fr1iY2PVvn17nTx50nfMsGHD9P7772vhwoVavXq1Tp06pc6dO8vt5j6wAABYx9DJnCidzImSDIeOh0fpeHiUZBhWBwMAIGAYpmmaVoc468iRI6pYsaJWrlypli1byjRNxcXFadiwYXryyScleWfVY2Ji9Nxzz2ngwIFKS0tTdHS05s2bpx49ekiSfvrpJ8XHx+ujjz5Shw4d/vB909PTFRUVpbS0NEVGRvr1MwIArJUwKsnqCMAF7Z/YyeoIAIBLoLA91FbfaU9LS5MklStXTpK0b98+paSk6JZbbvEdExISolatWmnt2rWSpI0bNyonJyfPMXFxcUpMTPQdAwAALr0gI0e960xX7zrTFe45o78tma6/LZmu4Nwcq6MBABAwbFPaTdPUiBEj1KJFCyUmJkqSUlJSJEkxMTF5jo2JifE9l5KSouDgYJUtW7bAY86VlZWl9PT0PA8AAFCyHA632lZNUtuqSXKZ2eqzOUl9NifJ6eHrawAAFJZtSvtjjz2mrVu3asGCBfmeM8757ptpmvn2netCx0yYMEFRUVG+R3x8fPGDAwAAAADgJ7Yo7YMHD9aHH36ozz//XJUrV/btj42NlaR8M+apqam+2ffY2FhlZ2frxIkTBR5zrtGjRystLc33OHToUEl+HAAAAAAASoSlpd00TT322GNavHixli9frmrVquV5vlq1aoqNjdXSpUt9+7Kzs7Vy5UrdcMMNkqTGjRvL5XLlOSY5OVnbt2/3HXOukJAQRUZG5nkAAAAAAGA3QVa++aBBg/TOO+/oP//5jyIiInwz6lFRUQoLC5NhGBo2bJjGjx+vmjVrqmbNmho/frzCw8PVs2dP37EDBgzQ448/rvLly6tcuXIaOXKk6tevr3bt2ln58QAAAAAAuCiWlvbp06dLklq3bp1n/6xZs9SvXz9J0hNPPKGMjAw9+uijOnHihK6//notWbJEERERvuOnTJmioKAgde/eXRkZGWrbtq1mz54tp9N5qT4KAAAAAAAlztLSXphbxBuGobFjx2rs2LEFHhMaGqqXX35ZL7/8cgmmAwAAAADAWpaWdgAAcPnKcQdr5Mo3JUknHaXV4s/ecaYr2MpYAAAEFEo7AADwC1MOHc349U4uhvRj1Pnv6gIAAApmi1u+AQAAAACA/JhpBwAAfuE0cnR3rXmSpP/svFfDVy6UJL3QsrdynC4rowEAEDCYaQcAAH7hdLh1W7XFuq3aYoWY2Rq4brEGrlusILfb6mgAAAQMSjsAAAAAADZFaQcAAAAAwKYo7QAAAAAA2BSlHQAAAAAAm6K0AwAAAABgU5R2AAAAAABsivu0AwAAv8hxB+up1a9Ikk46Sqt9f+840xVsZSwAAAIKpR0AAPiFKYd+OlXVu2FIu6OrWhsIAIAAxPJ4AAAAAABsipl2AADgF04jR12u+pck6ZPvu+rPa9+XJL3SvLtynC4rowEAEDAo7QAAwC+cDrfurLFAkvT5rk4atsY7nnFdN0o7AACFxPJ4AAAAAABsitIOAAAAAIBNUdoBAAAAALApSjsAAAAAADZFaQcAAAAAwKYo7QAAAAAA2BS3fAMAAH6R43Zp3NrJkqRTRrhu7+MdZwVxuzcAAAqL0g4AAPzClFP70mt5NxzS1kq1rA0EAEAAYnk8AAAAAAA2xUw7AADwC6eRo1uqfihJ+vyH29Rn/ceSpFlNbleOkyXyAAAUBqUdAAD4hdPhVo/asyRJa/e21VMrvON5DTtR2gEAKCSWxwMAAAAAYFOUdgAAAAAAbIrSDgAAAACATVHaAQAAAACwKUo7AAAAAAA2RWkHAAAAAMCmuOUbAADwixy3SxPXjZcknTLCde993nFWELd7AwCgsCjtAADAL0w59d3xBt4Nh/RVlQbWBgIAIACxPB4AAAAAAJtiph0AAPiF08hV6/hPJEmr97dT9y2fSZIWXHOrcp38EwQAgMLgb0wAAOAXTkeuetd9TZK0YV8L/X2pd7wosR2lHQCAQmJ5PAAAAAAANkVpBwAAAADApijtAAAAAADYFKUdAAAAAACborQDAAAAAGBTlHYAAAAAAGyK+60AAAC/yPW4NGXjGEnSGUe4HrjbO84OclkZCwCAgEJpBwAAfuExnfrmSFPvhiF9flVTawMBABCAWB4PAAAAAIBNMdMOAAD8wmnkqnncCknS+kMt1GXHaknSB3VbK9fJP0EAACgM/sYEAAB+4XTk6sH6UyVJ2w420QsfecdJV7egtAMAUEgsjwcAAAAAwKYo7QAAAAAA2BSlHQAAAAAAm6K0AwAAAABgU5R2AAAAAABsitIOAAAAAIBNcb8VAADgF7kel17ZPEqSdMYRrkfv8I6zg1xWxgIAIKBQ2gEAgF94TKfW/9zCu2FIH9VuYW0gAAACEMvjAQAAAACwKWbaAQCAXzgMtxpX/FKStCXlOrXftU6S9Gmt5nI7nFZGAwAgYFDaAQCAXwQ5cjSo4URJ0tCk+Xr1P95xneGLlBFMaQcAoDBYHg8AAAAAgE1R2gEAAAAAsCmWxwMAANhIwqgkqyNcdvZP7GR1BAAoNmbaAQAAAACwKUo7AAAAAAA2RWkHAAAAAMCm+E47AADwC7cnSG9sGyZJyjRCNbKjd5zj5J8fAAAUFn9rAgAAv3CbQVp9uJ13wyEtqt/O2kAAAAQglscDAAAAAGBTzLQDAAC/cBhu1a+wSZL0beo1avHDN5KkL6o1ktvhtDIaAAABg9IOAAD8IsiRo+GNx0mShibN16xF3nGd4YuUEUxpBwCgMFgeDwAAAACATVHaAQAAAACwKUo7AAAAAAA2RWkHAAAAAMCmKO0AAAAAANgUpR0AAAAAAJvilm8AAMAv3J4gzfv2z5KkTCNUT7f3jnOc/PMDAIDC4m9NAADgF24zSMsOdvZuOKR5jTpbGwgAgADE8ngAAAAAAGyKmXYAAOAXhty6utwOSdLuo7XV9MfvJEnrKteTx+G0MhoAAAGD0g4AAPzC5czRqOuekiQNTZqvhQu84zrDFykjmNIOAEBhsDweAAAAAACborQDAAAAAGBTlHYAAAAAAGyK0g4AAAAAgE1R2gEAAAAAsClKOwAAAAAANsUt3wAAgF+4PU69+90DkqQsI1jjW3vHuU5u9wYAQGFR2gHA5hJGJVkdASgWt+nSx/u7eTcc0szru1kbCACAAMTyeAAAAAAAbIqZdgAA4BeG3EqI3CtJOvhLgur9vF+StD3mKnkcLJEHAKAwKO0AAMAvXM4cjblhhCRpaNJ8fTjXO64zfJEygintAAAUBsvjAQAAAACwKUo7AAAAAAA2RWkHAAAAAMCmKO0AAAAAANgUpR0AAAAAAJuitAMAAAAAYFPc8g0AAPiF2+PUB3vukyRlGcGaeqN3nOvkdm8AABQWpR0AAPiF23Tpgz29vBsOaWqLXtYGAgAgALE8HgAAAAAAm2KmHQAA+IUhjyqVPiRJSjn5J1119LAkaU+FeJkG8wYAABQGpR0AAPiFy5mt8S0GSZKGJs3X0re84zrDFykjONTKaAAABAx+zQ0AAAAAgE1R2gEAAAAAsClKOwAAAAAANkVpBwAAAADApijtAAAAAADYFKUdAAAAAACb4pZvAADAL9wepz7ed5ckKcsI1ozrvONcp9PKWAAABBRKOwAA8Au36dK7u/p7NxzShDb9rQ0EAEAAYnk8AAAAAAA2xUw7AADwC0MelQ87Ikk6fqa84tKPSZIOR0bLNJg3AACgMCjtAADAL1zObL3QaoAkaWjSfK1+zTuuM3yRMoJDrYwGAEDAsPTX3F988YW6dOmiuLg4GYahDz74IM/z/fr1k2EYeR7NmjXLc0xWVpYGDx6sChUqqFSpUrr99tv1448/XsJPAQAAAACAf1ha2k+fPq1rrrlG06ZNK/CYW2+9VcnJyb7HRx99lOf5YcOG6f3339fChQu1evVqnTp1Sp07d5bb7fZ3fAAAAAAA/MrS5fG33XabbrvttgseExISotjY2PM+l5aWpjfffFPz5s1Tu3btJEnz589XfHy8PvvsM3Xo0KHEMwMAAAAAcKnY/iowK1asUMWKFVWrVi099NBDSk1N9T23ceNG5eTk6JZbbvHti4uLU2JiotauXWtFXAAAAAAASoytL0R322236Z577lHVqlW1b98+Pf3007r55pu1ceNGhYSEKCUlRcHBwSpbtmyen4uJiVFKSkqBr5uVlaWsrCzfdnp6ut8+AwAAAAAAxWXr0t6jRw/fODExUU2aNFHVqlWVlJSku+66q8CfM01ThmEU+PyECRM0bty4Es0KAAAAAEBJs/3y+N+rVKmSqlatqt27d0uSYmNjlZ2drRMnTuQ5LjU1VTExMQW+zujRo5WWluZ7HDp0yK+5AQC4Enk8Ti070EnLDnRSjhGsuQ07aW7DTnI7nFZHAwAgYNh6pv1cx44d06FDh1SpUiVJUuPGjeVyubR06VJ1795dkpScnKzt27dr0qRJBb5OSEiIQkJCLklmAACuVLmmS/N2PuLdcEjP3PKItYEAAAhAlpb2U6dOac+ePb7tffv2acuWLSpXrpzKlSunsWPHqlu3bqpUqZL279+vp556ShUqVFDXrl0lSVFRURowYIAef/xxlS9fXuXKldPIkSNVv35939XkAQAAAAAIVJaW9g0bNqhNmza+7REjRkiS+vbtq+nTp2vbtm2aO3eufvnlF1WqVElt2rTRu+++q4iICN/PTJkyRUFBQerevbsyMjLUtm1bzZ49W04nS+8AALCWqQiX92KvJ7MjVC7jpCTpeFikdIFrzwAAgN8YpmmaVoewWnp6uqKiopSWlqbIyEir4wBAHgmjkqyOABRLsDNTM9vfLUkamjRf37xwvySpzvBFyggOtTIarjD7J3ayOgIA5FPYHhpQF6IDAAAAAOBKQmkHAAAAAMCmKO0AAAAAANgUpR0AAAAAAJuitAMAAAAAYFOUdgAAAAAAbMrS+7QDAIDLl8fj1OrDbSVJOUawFiV6x26H08pYAAAEFEo7AADwi1zTpTe2DfduOKSRnYZbGwgAgADE8ngAAAAAAGyKmXYAAOAnpoKdWZKk7NxgheVkS5IyXCGSYVgZDACAgMFMOwAA8ItgZ5Zmtr9bM9vfrShPunZOuVs7p9ytsJwsq6MBABAwKO0AAAAAANgUpR0AAAAAAJuitAMAAAAAYFOUdgAAAAAAbIrSDgAAAACATVHaAQAAAACwKe7TDgAA/MI0HVqfcqMkKdcIUtLV3rHHwZwBAACFRWkHAAB+keMJ1itbRns3HNKgO0dbGwgAgADEr7oBAAAAALApSjsAAAAAADZFaQcAAH4R7MzU7Fs7a/atnRXl/kX7n+us/c91Vlh2ptXRAAAIGJR2AAAAAABsitIOAAAAAIBNUdoBAAAAALApSjsAAAAAADZFaQcAAAAAwKYo7QAAAAAA2FSQ1QEAAMDlyTQd+ia1iSQp1wjS8urescfBnAEAAIVFaQcAAH6R4wnWlE1jvRsOqf89Y62MAwBAQCryr7r37dvnjxwAAAAAAOAcRS7tNWrUUJs2bTR//nxlZmb6IxMAAAAAAFAxSvs333yjhg0b6vHHH1dsbKwGDhyodevW+SMbAAAIYMHOTM1o100z2nVTlPsXfTu5m76d3E1h2fzSHwCAwipyaU9MTNTkyZN1+PBhzZo1SykpKWrRooXq1aunyZMn68iRI/7ICQAAAlBIUJZCgrIkSeE5WQrPybI4EQAAgaXYl28NCgpS165d9a9//UvPPfec9u7dq5EjR6py5crq06ePkpOTSzInAAAAAABXnGKX9g0bNujRRx9VpUqVNHnyZI0cOVJ79+7V8uXLdfjwYd1xxx0lmRMAAAAAgCtOkW/5NnnyZM2aNUu7du1Sx44dNXfuXHXs2FGOX++5Wq1aNc2YMUO1a9cu8bAAAAAAAFxJilzap0+frv79++uBBx5QbGzseY+pUqWK3nzzzYsOBwAAAADAlazIpX337t1/eExwcLD69u1brEAAAAAAAMCryKV91qxZKl26tO655548+//973/rzJkzlHUAACBJMk1D3x1PlCS55dBX8d6xxzCsjAUAQEApcmmfOHGiXnvttXz7K1asqIcffpjSDgAAJEk5nhBNXDfRu+GU7u050dpAAAAEoCJfPf7AgQOqVq1avv1Vq1bVwYMHSyQUAAAAAAAoRmmvWLGitm7dmm//N998o/Lly5dIKAAAAAAAUIzSfu+992rIkCH6/PPP5Xa75Xa7tXz5cg0dOlT33nuvPzICAIAAFOzM1D9v7ql/3txTUe5ftPGfPbXxnz0Vlp1pdTQAAAJGkb/T/uyzz+rAgQNq27atgoK8P+7xeNSnTx+NHz++xAMCAIDAFRmc7huXz0i/wJEAAOB8ilzag4OD9e677+rvf/+7vvnmG4WFhal+/fqqWrWqP/IBAAAAAHDFKnJpP6tWrVqqVatWSWYBAAAAAAC/U+TS7na7NXv2bC1btkypqanyeDx5nl++fHmJhQMAAAAA4EpW5NI+dOhQzZ49W506dVJiYqIMw/BHLgAAAAAArnhFLu0LFy7Uv/71L3Xs2NEfeQAAAAAAwK+KdSG6GjVq+CMLAAC4jJimoR/SakqS3HLom1jv2MMqPQAACs0wTdMsyg+8+OKL+uGHHzRt2rTLZml8enq6oqKilJaWpsjISKvjAEAeCaOSrI4AAAFt/8ROVkcAgHwK20OLPNO+evVqff755/r4449Vr149uVyuPM8vXry46GkBAAAAAEA+RS7tZcqUUdeuXf2RBQAAAAAA/E6RS/usWbP8kQMAAFxmgh2ZGn/To5KkcZ9P0cczhkuS2j34qjJdoVZGAwAgYBS5tEtSbm6uVqxYob1796pnz56KiIjQTz/9pMjISJUuXbqkMwIAgEBkSBXCUiVJDtNU5XTv2CjS1XQAALiyFbm0HzhwQLfeeqsOHjyorKwstW/fXhEREZo0aZIyMzP12muv+SMnAAAAAABXHEdRf2Do0KFq0qSJTpw4obCwMN/+rl27atmyZSUaDgAAAACAK1mxrh6/Zs0aBQcH59lftWpVHT58uMSCAQAAAABwpSvyTLvH45Hb7c63/8cff1RERESJhAIAAAAAAMUo7e3bt9fUqVN924Zh6NSpUxozZow6duxYktkAAAAAALiiFXl5/JQpU9SmTRvVrVtXmZmZ6tmzp3bv3q0KFSpowYIF/sgIAAACkSkdPllFkuQxDH1f3js2DStDAQAQWAzTNIt845WMjAwtWLBAmzZtksfjUaNGjdSrV688F6YLJOnp6YqKilJaWpoiIyOtjgMAeSSMSrI6AgAEtP0TO1kdAQDyKWwPLdZ92sPCwtS/f3/179+/2AEBAAAAAMCFFbm0z50794LP9+nTp9hhAAAAAADAb4pc2ocOHZpnOycnR2fOnFFwcLDCw8Mp7QAAQJIU7MjUmOYjJEkTV03Qe2+NliTd3neyMl2hVkYDACBgFLm0nzhxIt++3bt365FHHtH//d//lUgoAABwGTCkP0UclCQ5TFO1jnnHRpGvpgMAwJWryLd8O5+aNWtq4sSJ+WbhAQAAAABA8ZVIaZckp9Opn376qaReDgAAAACAK16Rl8d/+OGHebZN01RycrKmTZumG2+8scSCAQAAAABwpStyab/zzjvzbBuGoejoaN1888168cUXSyoXAAAAAABXvCKXdo/H448cAAAAAADgHEUu7QAAAIViSkczKkqSPIahHyO9Y9OwMhQAAIGlyKV9xIgRhT528uTJRX15AABwmcj2hGrkyre8Gw6pxSNvWRsIAIAAVOTSvnnzZm3atEm5ubm6+uqrJUnff/+9nE6nGjVq5DvOMPg1OgAAAAAAF6PIpb1Lly6KiIjQnDlzVLZsWUnSiRMn9MADD+imm27S448/XuIhAQAAAAC4EhX5Pu0vvviiJkyY4CvsklS2bFk9++yzXD0eAAD4uBxZeqb5cD3TfLhKu9P1nznD9Z85wxWSk2V1NAAAAkaRZ9rT09P1888/q169enn2p6am6uTJkyUWDAAABDbDMFU9arckySmPrknxjh2maWUsAAACSpFn2rt27aoHHnhAixYt0o8//qgff/xRixYt0oABA3TXXXf5IyMAAAAAAFekIs+0v/baaxo5cqTuv/9+5eTkeF8kKEgDBgzQ888/X+IBAQAAAAC4UhW5tIeHh+vVV1/V888/r71798o0TdWoUUOlSpXyRz4AAAAAAK5YRV4ef1ZycrKSk5NVq1YtlSpVSibfTwMAAAAAoEQVubQfO3ZMbdu2Va1atdSxY0clJydLkh588EFu9wYAAAAAQAkqcmkfPny4XC6XDh48qPDwcN/+Hj166JNPPinRcAAAILClZ0cqPTtSknQsLFLHwiItTgQAQGAp8nfalyxZok8//VSVK1fOs79mzZo6cOBAiQUDAACBLdsdqiHL3/FuOKXGQ96xNhAAAAGoyDPtp0+fzjPDftbRo0cVEhJSIqEAAAAAAEAxSnvLli01d+5c37ZhGPJ4PHr++efVpk2bEg0HAAAAAMCVrMjL459//nm1bt1aGzZsUHZ2tp544gnt2LFDx48f15o1a/yREQAABCCXI0uPNxkjSZr25VN6493xkqS+94xTlovVeQAAFEaRS3vdunW1detWTZ8+XU6nU6dPn9Zdd92lQYMGqVKlSv7ICAAAApBhmKpdbrskySmPmh3yjh3cJhYAgEIrUmnPycnRLbfcohkzZmjcuHH+ygQAAAAAAFTE77S7XC5t375dhmH4Kw8AAAAAAPhVkS9E16dPH7355pv+yAIAAAAAAH6nyN9pz87O1htvvKGlS5eqSZMmKlWqVJ7nJ0+eXGLhAAAAAAC4khWqtG/dulWJiYlyOBzavn27GjVqJEn6/vvv8xzHsnkAAAAAAEpOoUp7w4YNlZycrIoVK+rAgQNav369ypcv7+9sAAAgwGXl/nZrtzPc5g0AgCIrVGkvU6aM9u3bp4oVK2r//v3yeDz+zgUAAAJctjtUAz97z7vhlOqOeM/aQAAABKBClfZu3bqpVatWqlSpkgzDUJMmTeR0Os977A8//FCiAQEAAAAAuFIVqrTPnDlTd911l/bs2aMhQ4booYceUkREhL+zAQAAAABwRSv01eNvvfVWSdLGjRs1dOhQSjsAALgglyNbj107XpI0c8NIvfzeC5KkR7o+paygYCujAQAQMIp8y7dZs2b5IwcAALjMGIZH11TcIEkKMnN18w/esYNr4wAAUGgOqwMAAAAAAIDzo7QDAAAAAGBTlHYAAAAAAGyK0g4AAAAAgE1R2gEAAAAAsClKOwAAAAAANlXkW74BAAAURrY7VP0++Z93wyklPPk/awMBABCAmGkHAAAAAMCmKO0AAAAAANgUy+MBAIBfuBzZerjBi5Kk2ZsGa9KHL0uSRnR+XFlBwVZGAwAgYDDTDgAA/MIwPGoau0ZNY9coyMxVp11r1GnXGjk8HqujAQAQMCjtAAAAAADYFKUdAAAAAACborQDAAAAAGBTlHYAAAAAAGzK0tL+xRdfqEuXLoqLi5NhGPrggw/yPG+apsaOHau4uDiFhYWpdevW2rFjR55jsrKyNHjwYFWoUEGlSpXS7bffrh9//PESfgoAAAAAAPzD0tJ++vRpXXPNNZo2bdp5n580aZImT56sadOmaf369YqNjVX79u118uRJ3zHDhg3T+++/r4ULF2r16tU6deqUOnfuLLfbfak+BgAAAAAAfmGYpmlaHUKSDMPQ+++/rzvvvFOSd5Y9Li5Ow4YN05NPPinJO6seExOj5557TgMHDlRaWpqio6M1b9489ejRQ5L0008/KT4+Xh999JE6dOhQqPdOT09XVFSU0tLSFBkZ6ZfPBwDFlTAqyeoIQDGZCnZmSZKyc4MVlpMtScpwhUiGYWUwXGH2T+xkdQQAyKewPdS232nft2+fUlJSdMstt/j2hYSEqFWrVlq7dq0kaePGjcrJyclzTFxcnBITE33HnE9WVpbS09PzPAAAQEkzlO0OVbY7VDIcyggOVUZwKIUdAIAisG1pT0lJkSTFxMTk2R8TE+N7LiUlRcHBwSpbtmyBx5zPhAkTFBUV5XvEx8eXcHoAAAAAAC6ebUv7WcY5v403TTPfvnP90TGjR49WWlqa73Ho0KESyQoAAH4TZOTowfpT9GD9KQr3nNELSVP0QtIUBefmWB0NAICAYdvSHhsbK0n5ZsxTU1N9s++xsbHKzs7WiRMnCjzmfEJCQhQZGZnnAQAASpbD4VaLPy1Tiz8tk8vM1t3bl+nu7cvk9HCxWAAACsu2pb1atWqKjY3V0qVLffuys7O1cuVK3XDDDZKkxo0by+Vy5TkmOTlZ27dv9x0DAAAAAECgCrLyzU+dOqU9e/b4tvft26ctW7aoXLlyqlKlioYNG6bx48erZs2aqlmzpsaPH6/w8HD17NlTkhQVFaUBAwbo8ccfV/ny5VWuXDmNHDlS9evXV7t27az6WAAAAAAAlAhLS/uGDRvUpk0b3/aIESMkSX379tXs2bP1xBNPKCMjQ48++qhOnDih66+/XkuWLFFERITvZ6ZMmaKgoCB1795dGRkZatu2rWbPni2n03nJPw8AAAAAACXJ0tLeunVrXeg28YZhaOzYsRo7dmyBx4SGhurll1/Wyy+/7IeEAAAAAABYx7bfaQcAAAAA4EpHaQcAAAAAwKYsXR4PAAAuX9nuEA1e9rYk6aQjQo0Ge8cZrhArYwEAEFAo7QAAwE8MncyJOjvU8fAoa+MAABCAWB4PAAAAAIBNMdMOAAD8IsjI0X2135Akvbejr0Z9NkeS9OzNDyo7yGVlNAAAAgYz7QAAwC8cDrfaVk1S26pJcpnZ6rM5SX02J8npcVsdDQCAgEFpBwAAAADApijtAAAAAADYFKUdAAAAAACborQDAAAAAGBTlHYAAAAAAGyK0g4AAAAAgE1xn3YAAOAXOe5gjVz5piTppKO0WvzZO850BVsZCwCAgEJpBwAAfmHKoaMZMd4NQ/oxKsbaQAAABCCWxwMAAAAAYFPMtAMAAL9wGjm6u9Y8SdJ/dt6r4SsXSpJeaNlbOU6XldEAAAgYzLQDAAC/cDrcuq3aYt1WbbFCzGwNXLdYA9ctVpDbbXU0AAACBqUdAAAAAACborQDAAAAAGBTlHYAAAAAAGyKC9EBAADgspYwKsnqCJed/RM7WR0BuGIw0w4AAAAAgE1R2gEAAAAAsCmWxwMAAL/IcQfrqdWvSJJOOkqrfX/vONMVbGUsAAACCqUdAAD4hSmHfjpV1bthSLujq1obCACAAMTyeAAAAAAAbIqZdgAA4BdOI0ddrvqXJOmT77vqz2vflyS90ry7cpwuK6MBABAwKO0AAMAvnA637qyxQJL0+a5OGrbGO55xXTdKOwAAhcTyeAAAAAAAbIrSDgAAAACATVHaAQAAAACwKUo7AAAAAAA2RWkHAAAAAMCmKO0AAAAAANgUt3wDAAB+keN2adzayZKkU0a4bu/jHWcFcbs3AAAKi9IOAAD8wpRT+9JreTcc0tZKtawNBABAAGJ5PAAAAAAANsVMOwAA8AunkaNbqn4oSfr8h9vUZ/3HkqRZTW5XjpMl8gAAFAalHQAA+IXT4VaP2rMkSWv3ttVTK7zjeQ07UdoBACgklscDAAAAAGBTlHYAAAAAAGyK0g4AAAAAgE1R2gEAAAAAsClKOwAAAAAANkVpBwAAAADAprjlGwAA8Isct0sT142XJJ0ywnXvfd5xVhC3ewMAoLAo7QAAwC9MOfXd8QbeDYf0VZUG1gYCACAAsTweAAAAAACbYqYdAAD4hdPIVev4TyRJq/e3U/ctn0mSFlxzq3Kd/BMEAIDC4G9MAADgF05HrnrXfU2StGFfC/19qXe8KLEdpR0AgEJieTwAAAAAADZFaQcAAAAAwKYo7QAAAAAA2BSlHQAAAAAAm6K0AwAAAABgU5R2AAAAAABsivutAAAAv8j1uDRl4xhJ0hlHuB642zvODnJZGQsAgIBCaQcAAH7hMZ365khT74YhfX5VU2sDAQAQgFgeDwAAAACATTHTDgAA/MJp5Kp53ApJ0vpDLdRlx2pJ0gd1WyvXyT9BAAAoDP7GBAAAfuF05OrB+lMlSdsONtELH3nHSVe3oLQDAFBILI8HAAAAAMCmKO0AAAAAANgUpR0AAAAAAJuitAMAAAAAYFOUdgAAAAAAbIrSDgAAAACATXG/FQAA4Be5Hpde2TxKknTGEa5H7/COs4NcVsYCACCgUNoBAIBfeEyn1v/cwrthSB/VbmFtIAAAAhDL4wEAAAAAsClm2gEAgF84DLcaV/xSkrQl5Tq137VOkvRpreZyO5xWRgMAIGBQ2gEAgF8EOXI0qOFESdLQpPl69T/ecZ3hi5QRTGkHAKAwWB4PAAAAAIBNUdoBAAAAALApSjsAAAAAADZFaQcAAAAAwKYo7QAAAAAA2BSlHQAAAAAAm+KWbwAAwC/cniC9sW2YJCnTCNXIjt5xjpN/fgAAUFj8rQkAAPzCbQZp9eF23g2HtKh+O2sDAQAQgFgeDwAAAACATTHTDgAA/MJhuFW/wiZJ0rep16jFD99Ikr6o1khuh9PKaAAABAxKOwAA8IsgR46GNx4nSRqaNF+zFnnHdYYvUkYwpR0AgMJgeTwAAAAAADZFaQcAAAAAwKYo7QAAAAAA2BTfaQdQohJGJVkdAQAAALhsMNMOAAAAAIBNUdoBAAAAALAplscDAAC/cHuCNO/bP0uSMo1QPd3eO85x8s8PAAAKi781AQCAX7jNIC072Nm74ZDmNepsbSAAAAIQy+MBAAAAALApZtoBAIBfGHLr6nI7JEm7j9ZW0x+/kyStq1xPHofTymgAAAQMSjsAAPALlzNHo657SpI0NGm+Fi7wjusMX6SMYEo7AACFwfJ4AAAAAABsitIOAAAAAIBNUdoBAAAAALApSjsAAAAAADZFaQcAAAAAwKYo7QAAAAAA2BS3fAMAAH7h9jj17ncPSJKyjGCNb+0d5zq53RsAAIVFaQcAAH7hNl36eH8374ZDmnl9N2sDAQAQgFgeDwAAAACATTHTDgAA/MKQWwmReyVJB39JUL2f90uStsdcJY+DJfIAABQGpR0AAPiFy5mjMTeMkCQNTZqvD+d6x3WGL1JGMKUdAIDCYHk8AAAAAAA2RWkHAAAAAMCmKO0AAAAAANgUpR0AAAAAAJuitAMAAAAAYFOUdgAAAAAAbIpbvgEAAL9we5z6YM99kqQsI1hTb/SOc53c7g0AgMKitAMAAL9wmy59sKeXd8MhTW3Ry9pAAAAEIJbHAwAAAABgU7Yu7WPHjpVhGHkesbGxvudN09TYsWMVFxensLAwtW7dWjt27LAwMQAAOMuQR3GlDyiu9AE5zFzVPHJANY8ckGF6rI4GAEDAsHVpl6R69eopOTnZ99i2bZvvuUmTJmny5MmaNm2a1q9fr9jYWLVv314nT560MDEAAJAklzNb41sM0vgWgxThOaWlbw3S0rcGKTQn2+poAAAEDNuX9qCgIMXGxvoe0dHRkryz7FOnTtVf/vIX3XXXXUpMTNScOXN05swZvfPOOxanBgAAAADg4tm+tO/evVtxcXGqVq2a7r33Xv3www+SpH379iklJUW33HKL79iQkBC1atVKa9euveBrZmVlKT09Pc8DAAAAAAC7sXVpv/766zV37lx9+umnev3115WSkqIbbrhBx44dU0pKiiQpJiYmz8/ExMT4nivIhAkTFBUV5XvEx8f77TMAAAAAAFBcti7tt912m7p166b69eurXbt2SkpKkiTNmTPHd4xhGHl+xjTNfPvONXr0aKWlpfkehw4dKvnwAAAAAABcJFuX9nOVKlVK9evX1+7du31XkT93Vj01NTXf7Pu5QkJCFBkZmecBAAAAAIDdBFRpz8rK0s6dO1WpUiVVq1ZNsbGxWrp0qe/57OxsrVy5UjfccIOFKQEAAAAAKBlBVge4kJEjR6pLly6qUqWKUlNT9eyzzyo9PV19+/aVYRgaNmyYxo8fr5o1a6pmzZoaP368wsPD1bNnT6ujAwBwxXN7nPp4312SpCwjWDOu845znU4rYwEAEFBsXdp//PFH3XfffTp69Kiio6PVrFkzffXVV6pataok6YknnlBGRoYeffRRnThxQtdff72WLFmiiIgIi5MDAAC36dK7u/p7NxzShDb9rQ0EAEAAMkzTNK0OYbX09HRFRUUpLS2N77cDFylhVJLVEQAAgJ/tn9jJ6ghAwCtsD7X1TDsAAAhchjwqH3ZEknT8THnFpR+TJB2OjJZpBNRldQAAsAylHQAA+IXLma0XWg2QJA1Nmq/Vr3nHdYYvUkZwqJXRAAAIGPyaGwAAAAAAm6K0AwAAAABgU5R2AAAAAABsitIOAAAAAIBNUdoBAAAAALApSjsAAAAAADbFLd8AAIBfeDxOLTvQSZKUYwRrbkPv2O1wWhkLAICAQmkHAAB+kWu6NG/nI94Nh/TMLY9YGwgAgADE8ngAAAAAAGyKmXYAAOAnpiJc6ZKkk9kRKpdxUpJ0PCxSMgwrgwEAEDAo7QAAwC+CnVl6uW0vSdLQpPna9PL9kqQ6wxcpIzjUymgAAAQMlscDAAAAAGBTlHYAAAAAAGyK0g4AAAAAgE1R2gEAAAAAsClKOwAAAAAANkVpBwAAAADAprjlGwAA8AuPx6nVh9tKknKMYC1K9I7dDqeVsQAACCiUdgAA4Be5pktvbBvu3XBIIzsNtzYQAAABiOXxAAAAAADYFDPtAADAT0wFO7MkSdm5wQrLyZYkZbhCJMOwMhgAAAGDmXYAAOAXwc4szWx/t2a2v1tRnnTtnHK3dk65W2E5WVZHAwAgYFDaAQAAAACwKUo7AAAAAAA2RWkHAAAAAMCmKO0AAAAAANgUpR0AAAAAAJuitAMAAAAAYFPcpx0AAPiFaTq0PuVGSVKuEaSkq71jj4M5AwAACovSDgAA/CLHE6xXtoz2bjikQXeOtjYQAAABiF91AwAAAABgU5R2AAAAAABsitIOAAD8ItiZqdm3dtbsWzsryv2L9j/XWfuf66yw7EyrowEAEDAo7QAAAAAA2BQXogMAAABQJAmjkqyOcFnZP7GT1RFgY8y0AwAAAABgU5R2AAAAAABsitIOAAAAAIBNUdoBAAAAALApLkQHAAD8wjQd+ia1iSQp1wjS8urescfBnAEAAIVFaQcAAH6R4wnWlE1jvRsOqf89Y62MAwBAQOJX3QAAAAAA2BSlHQAAAAAAm6K0AwAAvwh2ZmpGu26a0a6boty/6NvJ3fTt5G4Ky860OhoAAAGD77QDAAC/CQnK8o3Dc7IucCQAADgfZtoBAAAAALApSjsAAAAAADZFaQcAAAAAwKYo7QAAAAAA2BSlHQAAAAAAm+Lq8QAAwC9M09B3xxMlSW459FW8d+wxDCtjAQAQUCjtAADAL3I8IZq4bqJ3wynd23OitYEAAAhALI8HAAAAAMCmKO0AAAAAANgUpR0AAPhFsDNT/7y5p/55c09FuX/Rxn/21MZ/9lRYdqbV0QAACBh8px0AAPhNZHC6b1w+I/0CRwIAgPNhph0AAAAAAJuitAMAAAAAYFOUdgAAAAAAbIrSDgAAAACATVHaAQAAAACwKa4eDwAA/MI0Df2QVlOS5JZD38R6xx7DsDIWAAABxTBN07Q6hNXS09MVFRWltLQ0RUZGWh0Hl1jCqCSrIwAAAOAKtn9iJ6sjwAKF7aEsjwcAAAAAwKYo7QAAAAAA2BSlHQAA+EWwI1MvtOqvF1r1V4QnTaun99fq6f0VmpNpdTQAAAIGF6IDAAD+YUgVwlIlSQ7TVOV079i44q+mAwBA4THTDgAAAACATVHaAQAAAACwKUo7AAAAAAA2RWkHAAAAAMCmKO0AAAAAANgUV48HAAD+YUqHT1aRJHkMQ9+X945Nw8pQAAAEFko7AADwi2xPqP6y5lXvhkO65cFXrQ0EAEAAYnk8AAAAAAA2RWkHAAAAAMCmKO0AAMAvgh2Z+seNj+ofNz6qCE+alrzxqJa88ahCczKtjgYAQMDgO+0AAMA/DOlPEQclSQ7TVK1j3rFhWhkKAIDAwkw7AAAAAAA2RWkHAAAAAMCmKO0AAAAAANgUpR0AAAAAAJuitAMAAAAAYFNcPR4AAPiHKR3NqChJ8hiGfoz0jk3DylAAAAQWSjsAAPCLbE+oRq58y7vhkFo88pa1gQAACEAsjwcAAAAAwKYo7QAAAAAA2BSlHQAA+IXLkaVnmg/XM82Hq7Q7Xf+ZM1z/mTNcITlZVkcDACBg8J12AADgF4ZhqnrUbkmSUx5dk+IdO0zTylgAAAQUZtoBAAAAALApSjsAAAAAADZFaQcAAAAAwKYo7QAAAAAA2BSlHQAAAAAAm+Lq8QAAwG/SsyN942NhkRc4EgAAnA+lHQAA+EW2O1RDlr/j3XBKjYe8Y20gAAACEMvjAQAAAACwKWbaA0zCqCSrIwAAAAAoQfwbv+Ttn9jJ6gglhtIOAAD8wuXI0uNNxkiSpn35lN54d7wkqe8945TlCrEyGgAAAYPSDgAA/MIwTNUut12S5JRHzQ55xw7TtDIWAAABhe+0AwAAAABgU5R2AAAAAABsitIOAAAAAIBNUdoBAAAAALApSjsAAAAAADbF1eMBAIDfZOX+dmu3M9zmDQCAIqO0AwAAv8h2h2rgZ+95N5xS3RHvWRsIAIAAxPJ4AAAAAABsitIOAAAAAIBNsTweAAD4hcuRrceuHS9JmrlhpF5+7wVJ0iNdn1JWULCV0QAACBiUdgAA4BeG4dE1FTdIkoLMXN38g3fs8HisjAUAQEBheTwAAAAAADZFaQcAAAAAwKYum9L+6quvqlq1agoNDVXjxo21atUqqyMBAAAAAHBRLovS/u6772rYsGH6y1/+os2bN+umm27SbbfdpoMHD1odDQAAAACAYrssSvvkyZM1YMAAPfjgg6pTp46mTp2q+Ph4TZ8+3epoAAAAAAAUW8BfPT47O1sbN27UqFGj8uy/5ZZbtHbt2vP+TFZWlrKysnzbaWlpkqT09HT/BS0hnqwzVkcAAKBQ3M5MnT796zjrjM7+LevOOiOPyRXkAQD+Ewjd7mxG0zQveFzAl/ajR4/K7XYrJiYmz/6YmBilpKSc92cmTJigcePG5dsfHx/vl4wAAFypOr94dvSwos4OX+1jTRgAwBUjaqrVCQrv5MmTioqKKvD5gC/tZxmGkWfbNM18+84aPXq0RowY4dv2eDw6fvy4ypcvX+DPBIr09HTFx8fr0KFDioyMtDoOAgTnDYqKcwbFwXmDouKcQVFxzqA4rDpvTNPUyZMnFRcXd8HjAr60V6hQQU6nM9+sempqar7Z97NCQkIUEhKSZ1+ZMmX8FdESkZGR/I8KRcZ5g6LinEFxcN6gqDhnUFScMygOK86bC82wnxXwF6ILDg5W48aNtXTp0jz7ly5dqhtuuMGiVAAAAAAAXLyAn2mXpBEjRqh3795q0qSJmjdvrpkzZ+rgwYP685//bHU0AAAAAACK7bIo7T169NCxY8f0t7/9TcnJyUpMTNRHH32kqlWrWh3tkgsJCdGYMWPyLf8HLoTzBkXFOYPi4LxBUXHOoKg4Z1Acdj9vDPOPri8PAAAAAAAsEfDfaQcAAAAA4HJFaQcAAAAAwKYo7QAAAAAA2BSlHQAAAAAAm6K0B5Dp06erQYMGioyMVGRkpJo3b66PP/74gj/zyiuvqE6dOgoLC9PVV1+tuXPnXqK0sKMJEybIMAwNGzbsgsetXLlSjRs3VmhoqKpXr67XXnvt0gSELRXmvElOTlbPnj119dVXy+Fw/OE5hstbYc6ZxYsXq3379oqOjvb9nfbpp59eupCwncKcN6tXr9aNN96o8uXLKywsTLVr19aUKVMuXUjYSmH/XXPWmjVrFBQUpGuvvdavuWBfhTlnVqxYIcMw8j2+++67Sxf0HJfFLd+uFJUrV9bEiRNVo0YNSdKcOXN0xx13aPPmzapXr16+46dPn67Ro0fr9ddfV9OmTbVu3To99NBDKlu2rLp06XKp48Ni69ev18yZM9WgQYMLHrdv3z517NhRDz30kObPn681a9bo0UcfVXR0tLp163aJ0sIuCnveZGVlKTo6Wn/5y1/4B/QVrrDnzBdffKH27dtr/PjxKlOmjGbNmqUuXbro66+/VsOGDS9RWthFYc+bUqVK6bHHHlODBg1UqlQprV69WgMHDlSpUqX08MMPX6K0sIPCnjNnpaWlqU+fPmrbtq1+/vlnP6eDHRX1nNm1a5ciIyN929HR0f6K9oeYaQ8gXbp0UceOHVWrVi3VqlVL//jHP1S6dGl99dVX5z1+3rx5GjhwoHr06KHq1avr3nvv1YABA/Tcc89d4uSw2qlTp9SrVy+9/vrrKlu27AWPfe2111SlShVNnTpVderU0YMPPqj+/fvrhRdeuERpYRdFOW8SEhL00ksvqU+fPoqKirpECWE3RTlnpk6dqieeeEJNmzZVzZo1NX78eNWsWVP//e9/L1Fa2EVRzpuGDRvqvvvuU7169ZSQkKD7779fHTp00KpVqy5RWthBUc6ZswYOHKiePXuqefPmfk4HOyrOOVOxYkXFxsb6Hk6n088pC0ZpD1But1sLFy7U6dOnC/yfT1ZWlkJDQ/PsCwsL07p165STk3MpYsImBg0apE6dOqldu3Z/eOyXX36pW265Jc++Dh06aMOGDZw3V5iinDeAdHHnjMfj0cmTJ1WuXDk/JIOdXcx5s3nzZq1du1atWrXyQzLYVVHPmVmzZmnv3r0aM2aMn5PBrorz/5mGDRuqUqVKatu2rT7//HM/pvtjLI8PMNu2bVPz5s2VmZmp0qVL6/3331fdunXPe2yHDh30xhtv6M4771SjRo20ceNGvfXWW8rJydHRo0dVqVKlS5weVli4cKE2bdqk9evXF+r4lJQUxcTE5NkXExOj3NxczpsrSFHPG+Biz5kXX3xRp0+fVvfu3Us4GeysuOdN5cqVdeTIEeXm5mrs2LF68MEH/ZQQdlPUc2b37t0aNWqUVq1apaAgqs+VqKjnTKVKlTRz5kw1btxYWVlZmjdvntq2basVK1aoZcuWfk57fpy5Aebqq6/Wli1b9Msvv+i9995T3759tXLlyvMW96efflopKSlq1qyZTNNUTEyM+vXrp0mTJlm6vAOXzqFDhzR06FAtWbIk36qLCzEMI8+2aZrn3Y/LU3HPG1y5LvacWbBggcaOHav//Oc/qlixoh8Swo4u5rxZtWqVTp06pa+++kqjRo1SjRo1dN999/kpKeyiqOeM2+1Wz549NW7cONWqVesSJITdFOf/M1dffbWuvvpq33bz5s116NAhvfDCC5aVdpkIaG3btjUffvjhCx6TnZ1tHjp0yMzNzTVfffVVMyIiwnS73ZcoIaz0/vvvm5JMp9Ppe0gyDcMwnU6nmZubm+9nbrrpJnPIkCF59i1evNgMCgoys7OzL1V0WKg4583vtWrVyhw6dOilCQtbuJhzZuHChWZYWJj5v//97xImhh1c7P9rzvr73/9u1qpVy89pYQdFPWdOnDiR73jDMHz7li1bZtEnwaVSUv+fefbZZ83atWv7OW3BmGkPcKZpKisr64LHuFwuVa5cWZJ3eUjnzp3lcHA5gytB27ZttW3btjz7HnjgAdWuXVtPPvnkeVdcNG/ePN+FoJYsWaImTZrI5XL5NS/soTjnDa5sxT1nFixYoP79+2vBggXq1KnTpYgKGymp/9cU5t9CuDwU9ZyJjIzMd/yrr76q5cuXa9GiRapWrZrfM8NaJfX/mc2bN1v6FVFKewB56qmndNtttyk+Pl4nT57UwoULtWLFCn3yySeSpNGjR+vw4cO+e7F///33Wrduna6//nqdOHFCkydP1vbt2zVnzhwrPwYuoYiICCUmJubZV6pUKZUvX963/9zz5s9//rOmTZumESNG6KGHHtKXX36pN998UwsWLLjk+WGN4pw3krRlyxZJ3iu0HjlyRFu2bFFwcHCB193A5aM458yCBQvUp08fvfTSS2rWrJlSUlIkeS+Yyh0IrgzFOW9eeeUVValSRbVr15bkvW/7Cy+8oMGDB1/a8LBEUc8Zh8OR7/iKFSsqNDQ0335cnorz/5mpU6cqISFB9erVU3Z2tubPn6/33ntP77333iXPfxalPYD8/PPP6t27t5KTkxUVFaUGDRrok08+Ufv27SVJycnJOnjwoO94t9utF198Ubt27ZLL5VKbNm20du1aJSQkWPQJYEfnnjfVqlXTRx99pOHDh+uVV15RXFyc/vnPf3KPduRx7nkjKc+9tTdu3Kh33nlHVatW1f79+y9xOtjRuefMjBkzlJubq0GDBmnQoEG+/X379tXs2bMtSAg7Ove88Xg8Gj16tPbt26egoCBdddVVmjhxogYOHGhhStjJ+f5+Ai7k3HMmOztbI0eO1OHDhxUWFqZ69eopKSlJHTt2tCyjYZq/XmEKAAAAAADYCl9sBgAAAADApijtAAAAAADYFKUdAAAAAACborQDAAAAAGBTlHYAAAAAAGyK0g4AAAAAgE1R2gEAAAAAsClKOwAAAAAANkVpBwAAAADApijtAAAAAADYFKUdAAAAAACborQDAAAAAGBT/w/PQIhDg7GOCgAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "mu_true = 4.2\n", "sample_size = 100\n", @@ -221,35 +165,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "1ea7a6a0", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:12: SyntaxWarning: invalid escape sequence '\\l'\n", - "<>:19: SyntaxWarning: invalid escape sequence '\\m'\n", - "<>:12: SyntaxWarning: invalid escape sequence '\\l'\n", - "<>:19: SyntaxWarning: invalid escape sequence '\\m'\n", - "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_11968\\3093412318.py:12: SyntaxWarning: invalid escape sequence '\\l'\n", - " axis[0].set_title(\"Running Average of Estimates for $\\lambda$\")\n", - "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_11968\\3093412318.py:19: SyntaxWarning: invalid escape sequence '\\m'\n", - " axis[1].set_title(\"Running Average of Estimates for $\\mu$\")\n" - ] - }, - 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "lambda_true = 0.5\n", "mu_true = 4.2\n", diff --git a/book/estimation/Confidence_Intervals.ipynb b/book/estimation/Confidence_Intervals.ipynb index 2ba470f..b4cc548 100644 --- a/book/estimation/Confidence_Intervals.ipynb +++ b/book/estimation/Confidence_Intervals.ipynb @@ -22,7 +22,7 @@ "metadata": {}, "source": [ "```{note}\n", - "In practice we only use a single confidence interval, but we can visualise the meaning of a confidence interval by simulating many at once. In the code example below, confidence intervals that do not capture the true parameter are colored red.\n", + "In practice we only use a single confidence interval, but we can visualise the meaning of a confidence interval by simulating many at once. In the code example below, confidence intervals that do not capture the true parameter are coloured red.\n", "```" ] }, @@ -76,9 +76,9 @@ " sample = np.random.normal(loc=expectation, scale=standard_deviation, size=n)\n", "\n", " point_estimate = np.mean(sample)\n", - " standard_deviation = np.std(sample, ddof=1)\n", + " sample_std = np.std(sample, ddof=1)\n", " t = stats.t.ppf((1 + confidence_level) / 2, n - 1)\n", - " margin_of_error = t * (standard_deviation / np.sqrt(n))\n", + " margin_of_error = t * (sample_std / np.sqrt(n))\n", " ci_lower_bound = point_estimate - margin_of_error\n", " ci_upper_bound = point_estimate + margin_of_error\n", " point_estimates.append(point_estimate)\n", @@ -96,13 +96,10 @@ "\n", "plt.figure(figsize=(10, 18))\n", "\n", - "colors = plt.cm.viridis(np.linspace(0, 1, len(x_values)))\n", - "np.random.shuffle(colors)\n", - "\n", "for i, (x_val, point_est, lower_err, upper_err) in enumerate(\n", " zip(x_values, point_estimates, lower_errors, upper_errors)\n", "):\n", - " color = colors[i]\n", + " color = \"green\"\n", " if point_est - lower_err > expectation or point_est + upper_err < expectation:\n", " color = \"red\"\n", " plt.errorbar(\n", @@ -117,11 +114,11 @@ "\n", "# plot intervals\n", "plt.xlabel(\"Estimate\")\n", - "plt.title(\"Confidence Intervals for Standard normal Distribution\")\n", + "plt.title(\"Confidence Intervals for Normal Distribution\")\n", "plt.yticks(x_values, [f\"Sample {i}\" for i in x_values])\n", "plt.grid(True)\n", - "plt.axvline(x=expectation, color=\"r\", linestyle=\"--\", label=\"True Mean (0)\")\n", - "plt.tight_layout\n", + "plt.axvline(x=expectation, color=\"r\", linestyle=\"--\", label=\"True Mean\")\n", + "plt.tight_layout()\n", "plt.show()" ] }, @@ -156,9 +153,9 @@ " sample = np.random.normal(loc=expectation, scale=standard_deviation, size=n)\n", "\n", " point_estimate = np.mean(sample)\n", - " standard_deviation = np.std(sample, ddof=1)\n", + " sample_std = np.std(sample, ddof=1)\n", " t = stats.t.ppf((1 + confidence_level) / 2, n - 1)\n", - " margin_of_error = t * (standard_deviation / np.sqrt(n))\n", + " margin_of_error = t * (sample_std / np.sqrt(n))\n", " ci_lower_bound = point_estimate - margin_of_error\n", " ci_upper_bound = point_estimate + margin_of_error\n", " point_estimates.append(point_estimate)\n", @@ -200,7 +197,6 @@ " plt.xlabel(\"Estimate\")\n", " plt.xlim(x_lims[0], x_lims[1])\n", " plt.title(f\"Confidence Intervals{title}\")\n", - " plt.legend()\n", " plt.grid(True)\n", " plt.axvline(x=expectation, color=\"r\", linestyle=\"--\", label=\"True Mean\")\n", " plt.legend()\n", @@ -319,7 +315,7 @@ "id": "64511541", "metadata": {}, "source": [ - "The confidence level has an inverse relationship on the width of the confidence interval. A higher confidence level corresponds to a larger $t$ value, which means a wider interval. A lower confidence level corresponds to a narrower interval. Experiment with the values in the code below to see this relationship." + "The confidence level has a direct relationship on the width of the confidence interval. A higher confidence level corresponds to a larger $t$ value, which means a wider interval. A lower confidence level corresponds to a narrower interval. Experiment with the values in the code below to see this relationship." ] }, { @@ -368,9 +364,7 @@ "\n", "There is another method called the \"Wilson Method\" that can also be used to calculate an approximate confidence interval with large $n$ for a proportion. This method involves solving the following formula for $p$ to obtain upper and lower bounds:\n", "\n", - "$$\n", - "\\left( \\frac X n - p \\right)^2 - (z_{\\frac{\\alpha}{2}})^2 \\frac{p(1-p)}{n} < 0\n", - "$$\n", + "$$p_0 = \\frac{\\hat{p} + \\frac{z^2}{2n} \\pm z\\sqrt{\\frac{\\hat{p}(1-\\hat{p})}{n} + \\frac{z^2}{4n^2}}}{1 + \\frac{z^2}{n}} $$\n", "\n", "In both of these cases, the confidence level is only approximate, and the Wilson method achieves on average a better approximation. The code below simulates confidence intervals calculated using both methods." ] @@ -404,19 +398,16 @@ "def wilson_confidence_interval(x, n, confidence_level=0.95):\n", " alpha = 1 - confidence_level\n", " z = stats.norm.ppf(1 - alpha / 2)\n", - "\n", " p_hat = x / n\n", " z_squared = z**2\n", "\n", - " p_tilde = (p_hat + z_squared / (2 * n)) / (1 + z_squared / n)\n", - "\n", " denominator = 1 + z_squared / n\n", " sqrt_term = np.sqrt((p_hat * (1 - p_hat) + z_squared / (4 * n)) / n)\n", "\n", " ci_lower = (p_hat + z_squared / (2 * n) - z * sqrt_term) / denominator\n", " ci_upper = (p_hat + z_squared / (2 * n) + z * sqrt_term) / denominator\n", "\n", - " return p_tilde, ci_lower, ci_upper" + " return p_hat, ci_lower, ci_upper" ] }, { @@ -482,7 +473,7 @@ "# plot results\n", "num_samples = 100\n", "n = 1000\n", - "p_true = 0.15\n", + "p_true = 0.10\n", "confidence_level = 0.95\n", "\n", "naive_point, naive_lower, naive_upper, wilson_point, wilson_lower, wilson_upper = (\n", From 7a74339960d647e248cd84635ba1a626f6110cc8 Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Fri, 11 Jul 2025 11:49:26 -0400 Subject: [PATCH 14/22] add initial drafts of ecdf, avg,sd,correlation, clt, and law of large number sections --- book/_toc.yml | 4 + book/estimation/911_Calls_Estimation.ipynb | 40 ++-- book/estimation/Birth_Month_Estimation.ipynb | 34 ++-- book/estimation/Mean_Squared_Error.ipynb | 78 +------- book/estimation/avg_sd_correlation.ipynb | 133 ++++++++++++++ book/estimation/ecdf_kde.ipynb | 19 ++ .../misc/Central_Limit_Theorem.ipynb | 172 ++++++++++++++++++ book/estimation/misc/Law_of_Large_Nums.ipynb | 98 ++++++++++ 8 files changed, 457 insertions(+), 121 deletions(-) create mode 100644 book/estimation/avg_sd_correlation.ipynb create mode 100644 book/estimation/ecdf_kde.ipynb create mode 100644 book/estimation/misc/Central_Limit_Theorem.ipynb create mode 100644 book/estimation/misc/Law_of_Large_Nums.ipynb diff --git a/book/_toc.yml b/book/_toc.yml index f4a1cfd..6a6ef6f 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -16,6 +16,10 @@ parts: - file: estimation/Bias.ipynb - file: estimation/Mean_Squared_Error.ipynb - file: estimation/Confidence_Intervals.ipynb + - file: estimation/avg_sd_correlation.ipynb + - file: estimation/ecdf_kde.ipynb + - file: estimation/misc/Central_Limit_Theorem.ipynb + - file: estimation/misc/Law_of_Large_Nums.ipynb - caption: Miscellaneous chapters: - file: references.md diff --git a/book/estimation/911_Calls_Estimation.ipynb b/book/estimation/911_Calls_Estimation.ipynb index bcfa6fa..b080bb6 100644 --- a/book/estimation/911_Calls_Estimation.ipynb +++ b/book/estimation/911_Calls_Estimation.ipynb @@ -7,7 +7,7 @@ "source": [ "# 911 Calls\n", "\n", - "In this chapter we will be using data on 911 calls in Fernhaven to estimate the probability of having no 911 calls in a minute. This distribution is modeled using a Poisson distribution with probability mass function \n", + "In this section we will be using data on 911 calls in Fernhaven to estimate the probability of having no 911 calls in a minute. This distribution is modeled using a Poisson distribution with probability mass function \n", "\n", "$$\n", " p(k)=\\frac{\\mu^k}{k!}e^{-\\mu}\n", @@ -23,11 +23,7 @@ "cell_type": "code", "execution_count": null, "id": "7856f2b4", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -39,11 +35,7 @@ "cell_type": "code", "execution_count": null, "id": "c0d2f978", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Read in the data and extract calls between 6 and 7\n", @@ -83,11 +75,7 @@ "cell_type": "code", "execution_count": null, "id": "ea3c1dad", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ "p_hat = [counts.count(0) / 60.0 for counts in hourly_counts.values()]\n", @@ -121,11 +109,7 @@ "cell_type": "code", "execution_count": null, "id": "e940e960", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ "mle = [np.exp(-1 * (sum(counts) / 60.0)) for counts in hourly_counts.values()]\n", @@ -145,11 +129,7 @@ "cell_type": "code", "execution_count": null, "id": "7b91c8d1", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ "# compute the true parameter over the full dataset\n", @@ -196,8 +176,14 @@ } ], "metadata": { + "kernelspec": { + "display_name": "prob_stat_book", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "name": "python", + "version": "3.13.5" } }, "nbformat": 4, diff --git a/book/estimation/Birth_Month_Estimation.ipynb b/book/estimation/Birth_Month_Estimation.ipynb index 4e95093..d9752c6 100644 --- a/book/estimation/Birth_Month_Estimation.ipynb +++ b/book/estimation/Birth_Month_Estimation.ipynb @@ -7,7 +7,7 @@ "source": [ "# Birth Months\n", "\n", - "In this chapter we will be estimating the probability mass function of birth months. We will be doing this using a dataset. The first step to analyse this data is to perform *data wrangling*, which is the process of cleaning the dataset and (when necessary) converting it to a suitable format. In this case we will be isolating the birth month and getting several data-sets of size $100$ that we can compare later." + "In this section we will be estimating the probability mass function of birth months. We will be doing this using a dataset. The first step to analyse this data is to perform *data wrangling*, which is the process of cleaning the dataset and (when necessary) converting it to a suitable format. In this case we will be isolating the birth month and getting several data-sets of size $100$ that we can compare later." ] }, { @@ -30,11 +30,7 @@ "cell_type": "code", "execution_count": null, "id": "7856f2b4", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -62,11 +58,7 @@ "cell_type": "code", "execution_count": null, "id": "0cacbeec", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Read in the data and extract the birth month\n", @@ -97,11 +89,7 @@ "cell_type": "code", "execution_count": null, "id": "c8afa175", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ "df_sample1 = pd.DataFrame({\"Month\": sample1.values})\n", @@ -149,11 +137,7 @@ "cell_type": "code", "execution_count": null, "id": "d81f79be", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ "# naive model for months\n", @@ -216,8 +200,14 @@ } ], "metadata": { + "kernelspec": { + "display_name": "prob_stat_book", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "name": "python", + "version": "3.13.5" } }, "nbformat": 4, diff --git a/book/estimation/Mean_Squared_Error.ipynb b/book/estimation/Mean_Squared_Error.ipynb index 8a63d02..7332d20 100644 --- a/book/estimation/Mean_Squared_Error.ipynb +++ b/book/estimation/Mean_Squared_Error.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "dc5e6613", "metadata": {}, "outputs": [], @@ -25,44 +25,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "13ed32cf", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:29: SyntaxWarning: invalid escape sequence '\\h'\n", - "<>:31: SyntaxWarning: invalid escape sequence '\\l'\n", - "<>:29: SyntaxWarning: invalid escape sequence '\\h'\n", - "<>:31: SyntaxWarning: invalid escape sequence '\\l'\n", - "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_11728\\4002907151.py:29: SyntaxWarning: invalid escape sequence '\\h'\n", - " plt.title(\"Estimation $\\hat{\\lambda}$\")\n", - "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_11728\\4002907151.py:31: SyntaxWarning: invalid escape sequence '\\l'\n", - " plt.xlabel(\"$\\lambda$ estimates\")\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE = 0.0025\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "rng = np.random.default_rng()\n", "lambda_true = 0.5\n", "sample_size = 100\n", "samples = 1000\n", @@ -121,44 +88,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "57cb4bee", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE = 0.0100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:29: SyntaxWarning: invalid escape sequence '\\h'\n", - "<>:31: SyntaxWarning: invalid escape sequence '\\m'\n", - "<>:29: SyntaxWarning: invalid escape sequence '\\h'\n", - "<>:31: SyntaxWarning: invalid escape sequence '\\m'\n", - "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_11728\\575748422.py:29: SyntaxWarning: invalid escape sequence '\\h'\n", - " plt.title(\"Estimation $\\hat{\\mu}$\")\n", - "C:\\Users\\Stella\\AppData\\Local\\Temp\\ipykernel_11728\\575748422.py:31: SyntaxWarning: invalid escape sequence '\\m'\n", - " plt.xlabel(\"$\\mu$ estimates\")\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "rng = np.random.default_rng()\n", "mu_true = 2.5\n", "sample_size = 100\n", "samples = 1000\n", @@ -168,7 +102,7 @@ "\n", "# simulate samples\n", "for i in range(samples):\n", - " sample = rng.normal(loc=mu_true, scale=1.0, size=sample_size)\n", + " sample = np.random.normal(loc=mu_true, scale=1.0, size=sample_size)\n", "\n", " mle = sample.sum() / sample_size\n", " estimates.append(mle)\n", diff --git a/book/estimation/avg_sd_correlation.ipynb b/book/estimation/avg_sd_correlation.ipynb new file mode 100644 index 0000000..d4f53aa --- /dev/null +++ b/book/estimation/avg_sd_correlation.ipynb @@ -0,0 +1,133 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3fef70cc", + "metadata": {}, + "source": [ + "# Average, Sample Standard Deviation, Sample Correlation\n", + "\n", + "In this section we will be exploring estimators for the expectation, standard deviation and correlation and how accurate they are.\n", + "\n", + "Let's start by looking at the normal distribution. In the code below we calculate the sample average and sample standard deviation and compare it to the true parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d50679af", + "metadata": { + "tags": [ + "thebe-remove-input-init" + ] + }, + "outputs": [], + "source": [ + "import micropip\n", + "\n", + "await micropip.install(\"ipywidgets\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a137a302", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "from ipywidgets import interactive, fixed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3fba5ffc", + "metadata": {}, + "outputs": [], + "source": [ + "def update_plots(expectation_mu, standard_dev, n, num_samples):\n", + " estimates_expectation = []\n", + " estimates_standard_deviation = []\n", + "\n", + " # simulate samples\n", + " for i in range(num_samples):\n", + " sample = np.random.normal(loc=expectation_mu, scale=standard_dev, size=n)\n", + "\n", + " expectation = sample.sum() / n\n", + " standard_deviation = np.std(sample, ddof=1)\n", + " estimates_expectation.append(expectation)\n", + " estimates_standard_deviation.append(standard_deviation)\n", + "\n", + " estimates_exp = np.array(estimates_expectation)\n", + " mean_exp = estimates_exp.mean()\n", + " estimates_std = np.array(estimates_standard_deviation)\n", + " mean_std = estimates_std.mean()\n", + "\n", + " # plot estimates\n", + " plt.figure(figsize=(15, 9))\n", + " plt.hist(estimates_exp)\n", + " plt.title(\"Estimation $\\hat{\\mu}$\")\n", + " plt.ylabel(\"frequency\")\n", + " plt.xlabel(\"$\\mu$ estimates\")\n", + " plt.xlim(2.2, 2.8)\n", + "\n", + " plt.axvline(x=expectation_mu, color=\"r\", linestyle=\"--\")\n", + " plt.axvline(x=mean_exp, color=\"y\", linestyle=\"--\")\n", + " plt.legend([\"true value\", \"mean of estimates\"])\n", + "\n", + " plt.figure(figsize=(15, 9))\n", + " plt.hist(estimates_std)\n", + " plt.title(\"Estimation $\\sigma$\")\n", + " plt.ylabel(\"frequency\")\n", + " plt.xlabel(\"$\\sigma$ estimates\")\n", + " plt.xlim(0.8,1.2)\n", + "\n", + " plt.axvline(x=standard_dev, color=\"r\", linestyle=\"--\")\n", + " plt.axvline(x=mean_std, color=\"y\", linestyle=\"--\")\n", + " plt.legend([\"true value\", \"mean of estimates\"])\n", + "\n", + " plt.show()\n", + "\n", + "mu_true = 2.5\n", + "samples = 100\n", + "sd = 1.0\n", + "\n", + "slider_n = widgets.IntSlider(\n", + " value=100, min=100, max=1000, step=10, description=\"Sample Size\"\n", + ")\n", + "interactive_plot = interactive(\n", + " update_plots,\n", + " expectation_mu=fixed(mu_true),\n", + " standard_dev=fixed(sd),\n", + " n=slider_n,\n", + " num_samples=fixed(samples)\n", + ")\n", + "interactive_plot" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "prob_stat_book", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/estimation/ecdf_kde.ipynb b/book/estimation/ecdf_kde.ipynb new file mode 100644 index 0000000..d827def --- /dev/null +++ b/book/estimation/ecdf_kde.ipynb @@ -0,0 +1,19 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f44a373b", + "metadata": {}, + "source": [ + "# Histogram, Empirical Cumulative Distribution, Kernel Density Estimate" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/estimation/misc/Central_Limit_Theorem.ipynb b/book/estimation/misc/Central_Limit_Theorem.ipynb new file mode 100644 index 0000000..de665ae --- /dev/null +++ b/book/estimation/misc/Central_Limit_Theorem.ipynb @@ -0,0 +1,172 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f4b600f7", + "metadata": {}, + "source": [ + "# Central Limit Theorem\n", + "\n", + "This section will be covering the central limit theorem (CLT) for sums and averages. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d0cd369", + "metadata": { + "tags": [ + "thebe-remove-input-init" + ] + }, + "outputs": [], + "source": [ + "import micropip\n", + "\n", + "await micropip.install(\"ipywidgets\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44b6e199", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as ss\n", + "import ipywidgets as widgets\n", + "from ipywidgets import interactive, fixed" + ] + }, + { + "cell_type": "markdown", + "id": "fd0b9d5c", + "metadata": {}, + "source": [ + "## Central Limit Theorem (for sums)\n", + "\n", + "The CLT for sums stipulates that for a sequence $X_i,...,X_n$ of independent and identically distributed random variables with expectation $E[X]=\\mu$ and variance $Var(X)=\\sigma ^ 2$ with large enough $n$, $S_n=\\sum^n_{i=1}X_i$ approximately has a normal distribution.\n", + "\n", + "$$\n", + "S_n \\sim N(n\\mu,n\\sigma ^ 2)\n", + "$$\n", + "\n", + "To illustrate the CLT in action, let us consider die throws. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b7fe659", + "metadata": {}, + "outputs": [], + "source": [ + "def dice_sum_simulation(n, n_simulations):\n", + " dice_rolls = np.random.randint(1, 7, size=(n_simulations, n))\n", + " sums = np.sum(dice_rolls, axis=1)\n", + " return sums\n", + "\n", + "def update_plot(n_dice, n_sim):\n", + " mu_die = 3.5\n", + " var_die = 35/12 \n", + " \n", + " mu_sum = n_dice * mu_die \n", + " var_sum = n_dice * var_die\n", + " sigma_sum = np.sqrt(var_sum) \n", + " \n", + " sums = dice_sum_simulation(n_dice, n_sim)\n", + " \n", + " plt.figure(figsize=(12, 8))\n", + " plt.hist(sums, bins=50, density=True, alpha=0.7, color='skyblue', \n", + " edgecolor='black', label=f'Simulated Sums (n={n_sim})')\n", + " x = np.linspace(sums.min(), sums.max(), 1000)\n", + " normal_pdf = ss.norm.pdf(x, loc=mu_sum, scale=sigma_sum)\n", + " plt.plot(x, normal_pdf, 'r-', linewidth=3, \n", + " label=f'Normal Approx: N({mu_sum:.1f}, {sigma_sum:.2f}²)')\n", + " \n", + " plt.xlabel('Sum of Dice')\n", + " plt.ylabel('Density/Probability')\n", + " plt.title(f'Distribution of Sum of {n_dice} Dice Throws')\n", + " plt.legend()\n", + " plt.grid(True, alpha=0.3)\n", + "\n", + "amt_simulations = 10000\n", + "\n", + "slider_n = widgets.IntSlider(\n", + " value=2, min=2, max=150, step=2, description=\"Num dice\"\n", + ")\n", + "interactive_plot = interactive(\n", + " update_plot,\n", + " n_dice=slider_n,\n", + " n_sim=fixed(amt_simulations)\n", + ")\n", + "interactive_plot" + ] + }, + { + "cell_type": "markdown", + "id": "08486572", + "metadata": {}, + "source": [ + "## Central Limit Theorem (for averages)\n", + "\n", + "The CLT for averages stipulates that for a sequence $X_1,...,X_n$ of independent and identically distributed random variables with expectation $E[X]=\\mu$ and variance $Var(X)=\\sigma ^2$ with large enough $n$, $\\bar{X}_n = \\frac 1 n \\sum^n_{i=1} X_i$ approximately has a normal distribution.\n", + "\n", + "$$\n", + "\\bar{X}_n \\sim N(\\mu, \\frac{\\sigma ^ 2}{n})\n", + "$$\n", + "\n", + "To illustrate the CLT in action, let us consider an $Exp(1)$ distribution. We will plot the exact distribution and the CLT approximation with various $n$ values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e710428", + "metadata": {}, + "outputs": [], + "source": [ + "def update_plots(lambda_true, n):\n", + " x = np.linspace(0, 4.5, 1000)\n", + "\n", + " pdf_exact = ss.gamma.pdf(x, a=n, scale=1/(n*lambda_true))\n", + " pdf_normal = ss.norm.pdf(x, loc=1/lambda_true, scale=1/(lambda_true * np.sqrt(n))) \n", + "\n", + " plt.figure(figsize=(12,9))\n", + "\n", + " plt.plot(x, pdf_normal, label=f\"N({(1/lambda_true):.2f},{(1/(lambda_true * np.sqrt(n))):.2f})\", color=\"tab:orange\")\n", + " plt.plot(x, pdf_exact, label=f\"Mean of {n} i.i.d Exp({lambda_true})\", color=\"tab:blue\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + "\n", + "lambda_val = 1.0\n", + "\n", + "slider_n = widgets.IntSlider(\n", + " value=2, min=2, max=100, step=2, description=\"Size of n\"\n", + ")\n", + "interactive_plot = interactive(\n", + " update_plots,\n", + " n=slider_n,\n", + " lambda_true=fixed(lambda_val)\n", + ")\n", + "interactive_plot" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "prob_stat_book", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/estimation/misc/Law_of_Large_Nums.ipynb b/book/estimation/misc/Law_of_Large_Nums.ipynb new file mode 100644 index 0000000..20e22df --- /dev/null +++ b/book/estimation/misc/Law_of_Large_Nums.ipynb @@ -0,0 +1,98 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8497a600", + "metadata": {}, + "source": [ + "# Law of Large Numbers\n", + "\n", + "This section we will be looking at the law of large numbers. This theorem states that for an independent and identically distributed sequence $X_1,...,X_n$ with $E[X_i]=\\mu$ and $E[X_i^2]<\\infty$ for any $\\epsilon > 0$ the following holds:\n", + "\n", + "$$\n", + "P(|\\bar{X}_n-\\mu|>\\epsilon) \\rightarrow 0, \\text{ as } n \\rightarrow \\infty\n", + "$$\n", + "\n", + "We can illustrate this theorem by considering a simulation of throwing two dice. We will simulate the mean of the maximum value of both dice and the mean of the sum of both dice over a large number of throws." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60e026a2", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as ss" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c27ea0e2", + "metadata": {}, + "outputs": [], + "source": [ + "running_avg_max = []\n", + "running_avg_sum = []\n", + "throws = 1000\n", + "\n", + "for i in range(throws):\n", + " d1 = np.random.uniform(1,7,1).astype(int)\n", + " d2 = np.random.uniform(1,7,1).astype(int)\n", + "\n", + " if i==0:\n", + " running_avg_max.append(max(d1,d2))\n", + " running_avg_sum.append(d1 + d2)\n", + " else:\n", + " running_avg_max.append(\n", + " (running_avg_max[-1] * i + max(d1,d2)) / (i + 1)\n", + " )\n", + " running_avg_sum.append(\n", + " (running_avg_sum[-1] * i + (d1+d2)) / (i + 1)\n", + " )\n", + "\n", + "x = np.arange(1, throws + 1)\n", + "figure, axis = plt.subplots(2, 1, figsize=(14, 15))\n", + "axis[0].plot(x, running_avg_max, label=\"running average\")\n", + "axis[0].axhline(y=4.5, color=\"r\", linestyle=\"--\", label=\"true value\")\n", + "axis[0].set_title(\"Mean of max of two dice\")\n", + "axis[0].legend()\n", + "axis[0].set_xlabel(\"Throws\")\n", + "axis[0].set_ylabel(\"Mean number\")\n", + "\n", + "axis[1].plot(x, running_avg_sum, label=\"running average\")\n", + "axis[1].axhline(y=7, color=\"r\", linestyle=\"--\", label=\"true value\")\n", + "axis[1].set_title(\"Mean of sum of two dice\")\n", + "axis[1].legend()\n", + "axis[1].set_xlabel(\"Throws\")\n", + "axis[1].set_ylabel(\"Mean number\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2f93ffc2", + "metadata": {}, + "source": [ + "As this simulation illustrates, with large $n$, the average of the trial results converges to the expected value." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "prob_stat_book", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From d8eb0933a9125217a2b7687300a625141eab498c Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Fri, 11 Jul 2025 12:24:15 -0400 Subject: [PATCH 15/22] format all files using nbqa black for consistency --- book/estimation/Birth_Month_Estimation.ipynb | 2 +- book/estimation/Mean_Squared_Error.ipynb | 8 +- book/estimation/avg_sd_correlation.ipynb | 7 +- .../misc/Central_Limit_Theorem.ipynb | 83 +++++++++++-------- book/estimation/misc/Law_of_Large_Nums.ipynb | 16 ++-- 5 files changed, 63 insertions(+), 53 deletions(-) diff --git a/book/estimation/Birth_Month_Estimation.ipynb b/book/estimation/Birth_Month_Estimation.ipynb index d9752c6..4f5a604 100644 --- a/book/estimation/Birth_Month_Estimation.ipynb +++ b/book/estimation/Birth_Month_Estimation.ipynb @@ -148,7 +148,7 @@ "# estimate using entire dataset\n", "entire_dataset = pd.DataFrame({\"Month\": df[\"birth_month\"].values})\n", "\n", - "# sample from dataset \n", + "# sample from dataset\n", "sample_dataset = df[\"birth_month\"].sample(n=100)\n", "df_sample_dataset = pd.DataFrame({\"Month\": sample_dataset.values})\n", "\n", diff --git a/book/estimation/Mean_Squared_Error.ipynb b/book/estimation/Mean_Squared_Error.ipynb index 7332d20..9804922 100644 --- a/book/estimation/Mean_Squared_Error.ipynb +++ b/book/estimation/Mean_Squared_Error.ipynb @@ -20,7 +20,7 @@ "outputs": [], "source": [ "import numpy as np\n", - "import matplotlib.pyplot as plt\n" + "import matplotlib.pyplot as plt" ] }, { @@ -39,7 +39,7 @@ "\n", "# simulate samples\n", "for i in range(samples):\n", - " sample = np.random.exponential(scale=1/lambda_true, size=sample_size)\n", + " sample = np.random.exponential(scale=1 / lambda_true, size=sample_size)\n", "\n", " mle = sample_size / sample.sum()\n", " estimates.append(mle)\n", @@ -49,7 +49,7 @@ "estimates = np.array(estimates)\n", "mean_estimates = estimates.mean()\n", "errors = np.array(errors)\n", - "mse = np.mean(errors ** 2)\n", + "mse = np.mean(errors**2)\n", "\n", "print(f\"MSE = {mse:.4f}\")\n", "\n", @@ -112,7 +112,7 @@ "estimates = np.array(estimates)\n", "mean_estimates = estimates.mean()\n", "errors = np.array(errors)\n", - "mse = np.mean(errors ** 2)\n", + "mse = np.mean(errors**2)\n", "\n", "print(f\"MSE = {mse:.4f}\")\n", "\n", diff --git a/book/estimation/avg_sd_correlation.ipynb b/book/estimation/avg_sd_correlation.ipynb index d4f53aa..e4007bc 100644 --- a/book/estimation/avg_sd_correlation.ipynb +++ b/book/estimation/avg_sd_correlation.ipynb @@ -62,7 +62,7 @@ " estimates_standard_deviation.append(standard_deviation)\n", "\n", " estimates_exp = np.array(estimates_expectation)\n", - " mean_exp = estimates_exp.mean()\n", + " mean_exp = estimates_exp.mean()\n", " estimates_std = np.array(estimates_standard_deviation)\n", " mean_std = estimates_std.mean()\n", "\n", @@ -83,7 +83,7 @@ " plt.title(\"Estimation $\\sigma$\")\n", " plt.ylabel(\"frequency\")\n", " plt.xlabel(\"$\\sigma$ estimates\")\n", - " plt.xlim(0.8,1.2)\n", + " plt.xlim(0.8, 1.2)\n", "\n", " plt.axvline(x=standard_dev, color=\"r\", linestyle=\"--\")\n", " plt.axvline(x=mean_std, color=\"y\", linestyle=\"--\")\n", @@ -91,6 +91,7 @@ "\n", " plt.show()\n", "\n", + "\n", "mu_true = 2.5\n", "samples = 100\n", "sd = 1.0\n", @@ -103,7 +104,7 @@ " expectation_mu=fixed(mu_true),\n", " standard_dev=fixed(sd),\n", " n=slider_n,\n", - " num_samples=fixed(samples)\n", + " num_samples=fixed(samples),\n", ")\n", "interactive_plot" ] diff --git a/book/estimation/misc/Central_Limit_Theorem.ipynb b/book/estimation/misc/Central_Limit_Theorem.ipynb index de665ae..f14a63d 100644 --- a/book/estimation/misc/Central_Limit_Theorem.ipynb +++ b/book/estimation/misc/Central_Limit_Theorem.ipynb @@ -68,39 +68,49 @@ " sums = np.sum(dice_rolls, axis=1)\n", " return sums\n", "\n", + "\n", "def update_plot(n_dice, n_sim):\n", " mu_die = 3.5\n", - " var_die = 35/12 \n", - " \n", - " mu_sum = n_dice * mu_die \n", + " var_die = 35 / 12\n", + "\n", + " mu_sum = n_dice * mu_die\n", " var_sum = n_dice * var_die\n", - " sigma_sum = np.sqrt(var_sum) \n", - " \n", + " sigma_sum = np.sqrt(var_sum)\n", + "\n", " sums = dice_sum_simulation(n_dice, n_sim)\n", - " \n", + "\n", " plt.figure(figsize=(12, 8))\n", - " plt.hist(sums, bins=50, density=True, alpha=0.7, color='skyblue', \n", - " edgecolor='black', label=f'Simulated Sums (n={n_sim})')\n", + " plt.hist(\n", + " sums,\n", + " bins=50,\n", + " density=True,\n", + " alpha=0.7,\n", + " color=\"skyblue\",\n", + " edgecolor=\"black\",\n", + " label=f\"Simulated Sums (n={n_sim})\",\n", + " )\n", " x = np.linspace(sums.min(), sums.max(), 1000)\n", " normal_pdf = ss.norm.pdf(x, loc=mu_sum, scale=sigma_sum)\n", - " plt.plot(x, normal_pdf, 'r-', linewidth=3, \n", - " label=f'Normal Approx: N({mu_sum:.1f}, {sigma_sum:.2f}²)')\n", - " \n", - " plt.xlabel('Sum of Dice')\n", - " plt.ylabel('Density/Probability')\n", - " plt.title(f'Distribution of Sum of {n_dice} Dice Throws')\n", + " plt.plot(\n", + " x,\n", + " normal_pdf,\n", + " \"r-\",\n", + " linewidth=3,\n", + " label=f\"Normal Approx: N({mu_sum:.1f}, {sigma_sum:.2f}²)\",\n", + " )\n", + "\n", + " plt.xlabel(\"Sum of Dice\")\n", + " plt.ylabel(\"Density/Probability\")\n", + " plt.title(f\"Distribution of Sum of {n_dice} Dice Throws\")\n", " plt.legend()\n", " plt.grid(True, alpha=0.3)\n", "\n", + "\n", "amt_simulations = 10000\n", "\n", - "slider_n = widgets.IntSlider(\n", - " value=2, min=2, max=150, step=2, description=\"Num dice\"\n", - ")\n", + "slider_n = widgets.IntSlider(value=2, min=2, max=150, step=2, description=\"Num dice\")\n", "interactive_plot = interactive(\n", - " update_plot,\n", - " n_dice=slider_n,\n", - " n_sim=fixed(amt_simulations)\n", + " update_plot, n_dice=slider_n, n_sim=fixed(amt_simulations)\n", ")\n", "interactive_plot" ] @@ -131,27 +141,30 @@ "def update_plots(lambda_true, n):\n", " x = np.linspace(0, 4.5, 1000)\n", "\n", - " pdf_exact = ss.gamma.pdf(x, a=n, scale=1/(n*lambda_true))\n", - " pdf_normal = ss.norm.pdf(x, loc=1/lambda_true, scale=1/(lambda_true * np.sqrt(n))) \n", - "\n", - " plt.figure(figsize=(12,9))\n", - "\n", - " plt.plot(x, pdf_normal, label=f\"N({(1/lambda_true):.2f},{(1/(lambda_true * np.sqrt(n))):.2f})\", color=\"tab:orange\")\n", - " plt.plot(x, pdf_exact, label=f\"Mean of {n} i.i.d Exp({lambda_true})\", color=\"tab:blue\")\n", + " pdf_exact = ss.gamma.pdf(x, a=n, scale=1 / (n * lambda_true))\n", + " pdf_normal = ss.norm.pdf(\n", + " x, loc=1 / lambda_true, scale=1 / (lambda_true * np.sqrt(n))\n", + " )\n", + "\n", + " plt.figure(figsize=(12, 9))\n", + "\n", + " plt.plot(\n", + " x,\n", + " pdf_normal,\n", + " label=f\"N({(1/lambda_true):.2f},{(1/(lambda_true * np.sqrt(n))):.2f})\",\n", + " color=\"tab:orange\",\n", + " )\n", + " plt.plot(\n", + " x, pdf_exact, label=f\"Mean of {n} i.i.d Exp({lambda_true})\", color=\"tab:blue\"\n", + " )\n", " plt.legend()\n", " plt.show()\n", "\n", "\n", "lambda_val = 1.0\n", "\n", - "slider_n = widgets.IntSlider(\n", - " value=2, min=2, max=100, step=2, description=\"Size of n\"\n", - ")\n", - "interactive_plot = interactive(\n", - " update_plots,\n", - " n=slider_n,\n", - " lambda_true=fixed(lambda_val)\n", - ")\n", + "slider_n = widgets.IntSlider(value=2, min=2, max=100, step=2, description=\"Size of n\")\n", + "interactive_plot = interactive(update_plots, n=slider_n, lambda_true=fixed(lambda_val))\n", "interactive_plot" ] } diff --git a/book/estimation/misc/Law_of_Large_Nums.ipynb b/book/estimation/misc/Law_of_Large_Nums.ipynb index 20e22df..28580fe 100644 --- a/book/estimation/misc/Law_of_Large_Nums.ipynb +++ b/book/estimation/misc/Law_of_Large_Nums.ipynb @@ -40,19 +40,15 @@ "throws = 1000\n", "\n", "for i in range(throws):\n", - " d1 = np.random.uniform(1,7,1).astype(int)\n", - " d2 = np.random.uniform(1,7,1).astype(int)\n", + " d1 = np.random.uniform(1, 7, 1).astype(int)\n", + " d2 = np.random.uniform(1, 7, 1).astype(int)\n", "\n", - " if i==0:\n", - " running_avg_max.append(max(d1,d2))\n", + " if i == 0:\n", + " running_avg_max.append(max(d1, d2))\n", " running_avg_sum.append(d1 + d2)\n", " else:\n", - " running_avg_max.append(\n", - " (running_avg_max[-1] * i + max(d1,d2)) / (i + 1)\n", - " )\n", - " running_avg_sum.append(\n", - " (running_avg_sum[-1] * i + (d1+d2)) / (i + 1)\n", - " )\n", + " running_avg_max.append((running_avg_max[-1] * i + max(d1, d2)) / (i + 1))\n", + " running_avg_sum.append((running_avg_sum[-1] * i + (d1 + d2)) / (i + 1))\n", "\n", "x = np.arange(1, throws + 1)\n", "figure, axis = plt.subplots(2, 1, figsize=(14, 15))\n", From c3a12533dedda37abd431e7c728b12359233e8f3 Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Sat, 12 Jul 2025 10:00:32 -0400 Subject: [PATCH 16/22] add sliders in birth month and MLE as well as true value to plots, add section to mse with bad example --- book/estimation/Birth_Month_Estimation.ipynb | 119 ++++---- .../Maximum_Likelihood_Estimate.ipynb | 277 +++++++++++------- book/estimation/Mean_Squared_Error.ipynb | 138 ++++++++- 3 files changed, 373 insertions(+), 161 deletions(-) diff --git a/book/estimation/Birth_Month_Estimation.ipynb b/book/estimation/Birth_Month_Estimation.ipynb index 4f5a604..b9cea32 100644 --- a/book/estimation/Birth_Month_Estimation.ipynb +++ b/book/estimation/Birth_Month_Estimation.ipynb @@ -23,7 +23,8 @@ "source": [ "import micropip\n", "\n", - "await micropip.install(\"seaborn\")" + "await micropip.install(\"seaborn\")\n", + "await micropip.install(\"ipywidgets\")" ] }, { @@ -37,6 +38,8 @@ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", + "import ipywidgets as widgets\n", + "from ipywidgets import interactive, fixed\n", "\n", "months = [\n", " \"January\",\n", @@ -72,9 +75,9 @@ "df[\"birth_month\"] = df[\"ChildDOB\"].dt.month_name()\n", "\n", "# Sample three times from the dataset\n", - "sample1 = df[\"birth_month\"].sample(n=100, random_state=42)\n", - "sample2 = df[\"birth_month\"].sample(n=100, random_state=7)\n", - "sample3 = df[\"birth_month\"].sample(n=100, random_state=99)" + "sample1 = df[\"birth_month\"].sample(n=100)\n", + "sample2 = df[\"birth_month\"].sample(n=100)\n", + "sample3 = df[\"birth_month\"].sample(n=100)" ] }, { @@ -130,7 +133,7 @@ "source": [ "We can then compare this to more naïve models considered in the simulation chapter and to the estimate calculated when considering the entire dataset in the code below.\n", "\n", - "Feel free to play around with the size used in the naïve model and the sample from the dataset." + "Use the slider to adjust the size of the sample used in the naïve model and the dataset to see the impact of sample size on estimation." ] }, { @@ -140,56 +143,66 @@ "metadata": {}, "outputs": [], "source": [ - "# naive model for months\n", - "naive_months = np.random.choice(months, size=20000, p=[1 / 12 for i in range(12)])\n", - "\n", - "df_naive_months = pd.DataFrame(naive_months, columns=[\"Month\"])\n", - "\n", - "# estimate using entire dataset\n", - "entire_dataset = pd.DataFrame({\"Month\": df[\"birth_month\"].values})\n", - "\n", - "# sample from dataset\n", - "sample_dataset = df[\"birth_month\"].sample(n=100)\n", - "df_sample_dataset = pd.DataFrame({\"Month\": sample_dataset.values})\n", - "\n", - "\n", - "plt.figure(figsize=(15, 5))\n", - "\n", - "plt.subplot(1, 3, 1)\n", - "sns.countplot(x=\"Month\", data=df_naive_months, order=months, stat=\"proportion\")\n", - "plt.title(\"Distribution: Naive Uniform on Months\")\n", - "plt.xlabel(\"Month\")\n", - "plt.ylabel(\"Proportion\")\n", - "plt.xticks(rotation=45)\n", - "\n", - "\n", - "plt.subplot(1, 3, 2)\n", - "sns.countplot(\n", - " x=\"Month\",\n", - " data=entire_dataset,\n", - " order=months,\n", - " stat=\"proportion\",\n", + "def update_plot(n):\n", + "\n", + " # naive model for months\n", + " naive_months = np.random.choice(months, size=n, p=[1 / 12 for i in range(12)])\n", + " df_naive_months = pd.DataFrame(naive_months, columns=[\"Month\"])\n", + "\n", + " # estimate using entire dataset\n", + " entire_dataset = pd.DataFrame({\"Month\": df[\"birth_month\"].values})\n", + "\n", + " # sample from dataset\n", + " sample_dataset = df[\"birth_month\"].sample(n=n)\n", + " df_sample_dataset = pd.DataFrame({\"Month\": sample_dataset.values})\n", + "\n", + "\n", + " plt.figure(figsize=(15, 5))\n", + "\n", + " plt.subplot(1, 3, 1)\n", + " sns.countplot(x=\"Month\", data=df_naive_months, order=months, stat=\"proportion\")\n", + " plt.title(f\"Distribution: Naive Uniform on Months with n={n}\")\n", + " plt.xlabel(\"Month\")\n", + " plt.ylabel(\"Proportion\")\n", + " plt.xticks(rotation=45)\n", + "\n", + "\n", + " plt.subplot(1, 3, 2)\n", + " sns.countplot(\n", + " x=\"Month\",\n", + " data=entire_dataset,\n", + " order=months,\n", + " stat=\"proportion\",\n", + " )\n", + " plt.title(\"Distribution: Entire Dataset\")\n", + " plt.xlabel(\"Month\")\n", + " plt.ylabel(\"Proportion\")\n", + " plt.xticks(rotation=45)\n", + "\n", + "\n", + " plt.subplot(1, 3, 3)\n", + " sns.countplot(\n", + " x=\"Month\",\n", + " data=df_sample_dataset,\n", + " order=months,\n", + " stat=\"proportion\",\n", + " )\n", + " plt.title(f\"Distribution: Sample of Dataset with n={n}\")\n", + " plt.xlabel(\"Month\")\n", + " plt.ylabel(\"Proportion\")\n", + " plt.xticks(rotation=45)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "slider_n = widgets.IntSlider(\n", + " value=255, min=10, max=500, step=10, description=\"Sample Size\"\n", ")\n", - "plt.title(\"Distribution: Entire Dataset\")\n", - "plt.xlabel(\"Month\")\n", - "plt.ylabel(\"Proportion\")\n", - "plt.xticks(rotation=45)\n", - "\n", - "\n", - "plt.subplot(1, 3, 3)\n", - "sns.countplot(\n", - " x=\"Month\",\n", - " data=df_sample_dataset,\n", - " order=months,\n", - " stat=\"proportion\",\n", + "interactive_plot = interactive(\n", + " update_plot,\n", + " n=slider_n,\n", ")\n", - "plt.title(\"Distribution: Sample of Dataset with n=100\")\n", - "plt.xlabel(\"Month\")\n", - "plt.ylabel(\"Proportion\")\n", - "plt.xticks(rotation=45)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" + "interactive_plot" ] }, { diff --git a/book/estimation/Maximum_Likelihood_Estimate.ipynb b/book/estimation/Maximum_Likelihood_Estimate.ipynb index d1516bc..7565265 100644 --- a/book/estimation/Maximum_Likelihood_Estimate.ipynb +++ b/book/estimation/Maximum_Likelihood_Estimate.ipynb @@ -135,119 +135,167 @@ { "cell_type": "code", "execution_count": null, - "id": "0cc6e1a6", + "id": "1aa2cd12", "metadata": { - "vscode": { - "languageId": "plaintext" - } + "tags": [ + "thebe-remove-input-init" + ] }, "outputs": [], + "source": [ + "import micropip\n", + "\n", + "await micropip.install(\"ipywidgets\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cc6e1a6", + "metadata": {}, + "outputs": [], "source": [ "import numpy as np\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "from ipywidgets import interactive, fixed" ] }, { "cell_type": "code", "execution_count": null, "id": "f9d75c92", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ - "rng = np.random.default_rng()\n", - "data_exp = rng.exponential(scale=2.0, size=50)\n", - "\n", - "lambdas = np.linspace(0.01, 1.5, 500)\n", - "\n", - "# log likelihood for lambda\n", - "n = data_exp.size\n", - "sum_x = data_exp.sum()\n", - "logL = n * np.log(lambdas) - lambdas * sum_x\n", - "\n", - "\n", - "plt.figure()\n", - "plt.plot(lambdas, logL, lw=2)\n", - "plt.axvline(n / sum_x, color=\"C1\", ls=\"--\", label=f\"MLE $\\lambda$={n/sum_x:.2f}\")\n", - "plt.title(\"Exponential Likelihood\")\n", - "plt.xlabel(\"$\\lambda$\")\n", - "plt.ylabel(\"$L(\\lambda)$\")\n", - "plt.legend()\n", - "plt.show()" + "def update_plot(sample_size, lambda_val):\n", + " rng = np.random.default_rng()\n", + " data_exp = rng.exponential(scale=1/lambda_val, size=sample_size)\n", + "\n", + " lambdas = np.linspace(0.01, 1.5, 500)\n", + "\n", + " # log likelihood for lambda\n", + " n = data_exp.size\n", + " sum_x = data_exp.sum()\n", + " logL = n * np.log(lambdas) - lambdas * sum_x\n", + "\n", + "\n", + " plt.figure()\n", + " plt.plot(lambdas, logL, lw=2)\n", + " plt.axvline(n / sum_x, color=\"C1\", ls=\"--\", label=f\"MLE $\\lambda$={n/sum_x:.2f}\")\n", + " plt.axvline(lambda_val, color=\"tab:olive\", ls=\"--\", label=f\"True value $\\lambda$={lambda_val:.2f}\")\n", + " plt.title(\"Exponential Likelihood\")\n", + " plt.xlabel(\"$\\lambda$\")\n", + " plt.ylabel(\"$L(\\lambda)$\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + "lambda_true = 0.5\n", + "slider_n = widgets.IntSlider(\n", + " value=50, min=10, max=200, step=10, description=\"Sample Size\"\n", + ")\n", + "interactive_plot = interactive(\n", + " update_plot,\n", + " sample_size=slider_n,\n", + " \tlambda_val=fixed(lambda_true)\n", + ")\n", + "interactive_plot" ] }, { "cell_type": "code", "execution_count": null, "id": "0e1686bb", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ - "rng = np.random.default_rng()\n", - "data_norm = rng.normal(loc=1.0, scale=2.0, size=50)\n", - "mu = np.linspace(-2, 5, 200)\n", - "\n", - "# log likelihood for mu\n", - "sigma0 = data_norm.std(ddof=0)\n", - "logL_mu = [\n", - " (-len(data_norm) * np.log(sigma0) - np.sum((data_norm - m) ** 2) / (2 * sigma0**2))\n", - " for m in mu\n", - "]\n", - "\n", - "plt.figure()\n", - "plt.plot(mu, logL_mu)\n", - "plt.axvline(\n", - " data_norm.mean(), color=\"r\", ls=\"--\", label=f\"MLE $\\mu$={data_norm.mean():.2f}\"\n", + "def update_plot_mu(n, mu_val):\n", + " rng = np.random.default_rng()\n", + " data_norm = rng.normal(loc=mu_val, scale=2.0, size=n)\n", + " mu = np.linspace(-2, 5, 200)\n", + "\n", + " # log likelihood for mu\n", + " sigma0 = data_norm.std(ddof=0)\n", + " logL_mu = [\n", + " (-len(data_norm) * np.log(sigma0) - np.sum((data_norm - m) ** 2) / (2 * sigma0**2))\n", + " for m in mu\n", + " ]\n", + "\n", + " plt.figure()\n", + " plt.plot(mu, logL_mu)\n", + " plt.axvline(\n", + " data_norm.mean(), color=\"r\", ls=\"--\", label=f\"MLE $\\mu$={data_norm.mean():.2f}\"\n", + " )\n", + " plt.axvline(\n", + " mu_val, color=\"tab:olive\", ls=\"--\", label=f\"True value $\\mu$={mu_val:.2f}\"\n", + " )\n", + " plt.title(\"Log-Likelihood vs $\\mu$\")\n", + " plt.xlabel(\"$\\mu$\")\n", + " plt.ylabel(\"$\\ln L(\\mu)$\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + "mu_true = 1.0\n", + "slider_n = widgets.IntSlider(\n", + " value=50, min=10, max=200, step=10, description=\"Sample Size\"\n", + ")\n", + "interactive_plot = interactive(\n", + " update_plot_mu,\n", + " n=slider_n,\n", + " \tmu_val=fixed(mu_true)\n", ")\n", - "plt.title(\"Log-Likelihood vs $\\mu$\")\n", - "plt.xlabel(\"$\\mu$\")\n", - "plt.ylabel(\"$\\ln L(\\mu)$\")\n", - "plt.legend()\n", - "plt.show()" + "interactive_plot" ] }, { "cell_type": "code", "execution_count": null, "id": "c5f5b9a5", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ - "rng = np.random.default_rng()\n", - "data_norm = rng.normal(loc=1.0, scale=2.0, size=50)\n", - "sigma = np.linspace(0.1, 5, 200)\n", - "\n", - "# log likelihood for sigma\n", - "mu0 = data_norm.mean()\n", - "logL_sigma = [\n", - " -len(data_norm) * np.log(s) - np.sum((data_norm - mu0) ** 2) / (2 * s**2)\n", - " for s in sigma\n", - "]\n", - "\n", - "plt.figure()\n", - "plt.plot(sigma, logL_sigma)\n", - "plt.axvline(\n", - " np.sqrt(np.sum((data_norm - mu0) ** 2) / len(data_norm)),\n", - " color=\"r\",\n", - " ls=\"--\",\n", - " label=f\"MLE $\\sigma$={np.sqrt(((data_norm-mu0)**2).mean()):.2f}\",\n", + "def update_plot_sigma(n, sigma_val):\n", + " rng = np.random.default_rng()\n", + " data_norm = rng.normal(loc=1.0, scale=sigma_val, size=n)\n", + " sigma = np.linspace(0.1, 5, 200)\n", + "\n", + " # log likelihood for sigma\n", + " mu0 = data_norm.mean()\n", + " logL_sigma = [\n", + " -len(data_norm) * np.log(s) - np.sum((data_norm - mu0) ** 2) / (2 * s**2)\n", + " for s in sigma\n", + " ]\n", + "\n", + " plt.figure()\n", + " plt.plot(sigma, logL_sigma)\n", + " plt.axvline(\n", + " np.sqrt(np.sum((data_norm - mu0) ** 2) / len(data_norm)),\n", + " color=\"r\",\n", + " ls=\"--\",\n", + " label=f\"MLE $\\sigma$={np.sqrt(((data_norm-mu0)**2).mean()):.2f}\",\n", + " )\n", + " plt.axvline(\n", + " sigma_val,\n", + " color=\"tab:olive\",\n", + " ls=\"--\",\n", + " label=f\"True value $\\sigma$={sigma_val:.2f}\",\n", + " )\n", + " plt.title(\"Log-Likelihood vs $\\sigma$\")\n", + " plt.xlabel(\"$\\sigma$\")\n", + " plt.ylabel(\"$\\ln L(\\sigma)$\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + "sigma_true = 2.0\n", + "slider_n = widgets.IntSlider(\n", + " value=50, min=10, max=200, step=10, description=\"Sample Size\"\n", ")\n", - "plt.title(\"Log-Likelihood vs $\\sigma$\")\n", - "plt.xlabel(\"$\\sigma$\")\n", - "plt.ylabel(\"$\\ln L(\\sigma)$\")\n", - "plt.legend()\n", - "plt.show()" + "interactive_plot = interactive(\n", + " update_plot_sigma,\n", + " n=slider_n,\n", + " \tsigma_val=fixed(sigma_true)\n", + ")\n", + "interactive_plot" ] }, { @@ -284,38 +332,53 @@ "cell_type": "code", "execution_count": null, "id": "93522804", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ - "rng = np.random.default_rng()\n", - "data_exp = rng.exponential(scale=2.0, size=50)\n", - "\n", - "exp = np.linspace(0.01, 1.5, 500)\n", - "\n", - "# log likelihood for expectation of exponential distribution\n", - "n = data_exp.size\n", - "sum_x = data_exp.sum()\n", - "logL = -n * np.log(exp) - sum_x / exp\n", - "\n", - "\n", - "plt.figure()\n", - "plt.plot(exp, logL, lw=2)\n", - "plt.axvline(n / sum_x, color=\"C1\", ls=\"--\", label=f\"MLE $E[X]$={sum_x/n:.2f}\")\n", - "plt.title(\"Exponential Likelihood\")\n", - "plt.xlabel(\"$E[X]$\")\n", - "plt.ylabel(\"$L(E[X])$\")\n", - "plt.legend()\n", - "plt.show()" + "def update_plot_exp(sample_size, lambda_val):\n", + " rng = np.random.default_rng()\n", + " data_exp = rng.exponential(scale=1/lambda_val, size=sample_size)\n", + "\n", + " exp = np.linspace(0.01, 2.5, 500)\n", + "\n", + " # log likelihood for expectation of exponential distribution\n", + " n = data_exp.size\n", + " sum_x = data_exp.sum()\n", + " logL = -n * np.log(exp) - sum_x / exp\n", + "\n", + "\n", + " plt.figure()\n", + " plt.plot(exp, logL, lw=2)\n", + " plt.axvline(sum_x/n, color=\"C1\", ls=\"--\", label=f\"MLE $E[X]$={sum_x/n:.2f}\")\n", + " plt.axvline(1/lambda_val, color=\"tab:olive\", ls=\"--\", label=f\"True value $E[X]$={1/lambda_val:.2f}\")\n", + " plt.title(\"Exponential Likelihood\")\n", + " plt.xlabel(\"$E[X]$\")\n", + " plt.ylabel(\"$L(E[X])$\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + "lambda_true = 0.5\n", + "slider_n = widgets.IntSlider(\n", + " value=50, min=10, max=200, step=10, description=\"Sample Size\"\n", + ")\n", + "interactive_plot = interactive(\n", + " update_plot_exp,\n", + " sample_size=slider_n,\n", + " \tlambda_val=fixed(lambda_true)\n", + ")\n", + "interactive_plot" ] } ], "metadata": { + "kernelspec": { + "display_name": "prob_stat_book", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "name": "python", + "version": "3.13.5" } }, "nbformat": 4, diff --git a/book/estimation/Mean_Squared_Error.ipynb b/book/estimation/Mean_Squared_Error.ipynb index 9804922..87cab73 100644 --- a/book/estimation/Mean_Squared_Error.ipynb +++ b/book/estimation/Mean_Squared_Error.ipynb @@ -12,6 +12,22 @@ "We will start by simulating the exponential distribution to estimate $\\lambda$ and plotting histograms of the estimates as well as the errors in the simulation." ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe4fe5f6", + "metadata": { + "tags": [ + "thebe-remove-input-init" + ] + }, + "outputs": [], + "source": [ + "import micropip\n", + "\n", + "await micropip.install(\"ipywidgets\")" + ] + }, { "cell_type": "code", "execution_count": null, @@ -20,7 +36,9 @@ "outputs": [], "source": [ "import numpy as np\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "from ipywidgets import interactive, fixed" ] }, { @@ -140,6 +158,124 @@ "\n", "plt.show()" ] + }, + { + "cell_type": "markdown", + "id": "77e5625c", + "metadata": {}, + "source": [ + "Let us now consider the bad example where we use the minimum of a sample times the sample size to estimate the expectation of the exponential distribution. We compare this estimate to that of the maximum likelihood estimator in the code below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b20e8bef", + "metadata": {}, + "outputs": [], + "source": [ + "def update_plot_exp(sample_size, lambda_val):\n", + " rng = np.random.default_rng()\n", + " data_exp = rng.exponential(scale=1/lambda_val, size=sample_size)\n", + " min_val = data_exp.min()\n", + " bad_estimate = min_val * sample_size\n", + "\n", + " exp = np.linspace(0.01, 2.5, 500)\n", + "\n", + " n = data_exp.size\n", + " sum_x = data_exp.sum()\n", + " logL = -n * np.log(exp) - sum_x / exp\n", + "\n", + "\n", + " plt.figure()\n", + " plt.plot(exp, logL, lw=2)\n", + " plt.axvline(sum_x/n, color=\"C1\", ls=\"--\", label=f\"MLE $E[X]$={sum_x/n:.2f}\")\n", + " plt.axvline(1/lambda_val, color=\"tab:olive\", ls=\"--\", label=f\"True value $E[X]$={1/lambda_val:.2f}\")\n", + " plt.axvline(bad_estimate, color=\"tab:green\", ls=\"--\", label=f\"minimum x sample size $E[X]$={bad_estimate:.2f}\")\n", + " plt.title(\"Exponential Likelihood\")\n", + " plt.xlabel(\"$E[X]$\")\n", + " plt.ylabel(\"$L(E[X])$\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + "lambda_true = 0.5\n", + "slider_n = widgets.IntSlider(\n", + " value=50, min=10, max=200, step=10, description=\"Sample Size\"\n", + ")\n", + "interactive_plot = interactive(\n", + " update_plot_exp,\n", + " sample_size=slider_n,\n", + " \tlambda_val=fixed(lambda_true)\n", + ")\n", + "interactive_plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "890864fd", + "metadata": {}, + "outputs": [], + "source": [ + "lambda_true = 0.5\n", + "sample_size = 100\n", + "samples = 1000\n", + "\n", + "estimates = []\n", + "errors = []\n", + "bad_estimates = []\n", + "errors_bad = []\n", + "\n", + "# simulate samples\n", + "for i in range(samples):\n", + " sample = np.random.exponential(scale=1 / lambda_true, size=sample_size)\n", + "\n", + " mle = sample_size / sample.sum()\n", + " estimates.append(mle)\n", + " error = mle - lambda_true\n", + " errors.append(error)\n", + "\n", + " bad_estimate = sample.min() * sample_size\n", + " bad_estimates.append(bad_estimate)\n", + " error_bad = bad_estimate - lambda_true\n", + " errors_bad.append(error_bad)\n", + "\n", + "estimates = np.array(estimates)\n", + "mean_estimates = estimates.mean()\n", + "errors = np.array(errors)\n", + "mse = np.mean(errors**2)\n", + "bad_estimates = np.array(bad_estimates)\n", + "bad_mean_estimate = bad_estimates.mean()\n", + "errors_bad = np.array(errors_bad)\n", + "mse_bad = np.mean(errors_bad**2)\n", + "\n", + "print(f\"MSE = {mse:.4f}\")\n", + "print(f\"MSE bad = {mse_bad:.4f}\")\n", + "\n", + "# plot estimates\n", + "plt.figure(figsize=(15, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(errors)\n", + "plt.title(\"Errors MSE\")\n", + "plt.ylabel(\"frequency\")\n", + "plt.xlabel(\"Error (estimate - true value)\")\n", + "\n", + "plt.axvline(x=0, color=\"r\", linestyle=\"--\")\n", + "plt.axvline(x=np.mean(mse), color=\"y\", linestyle=\"--\")\n", + "plt.legend([\"zero error\", \"mean error\"])\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(errors_bad)\n", + "plt.title(\"Errors minimum x sample size\")\n", + "plt.ylabel(\"frequency\")\n", + "plt.xlabel(\"Error (bad estimate - true value)\")\n", + "\n", + "plt.axvline(x=0, color=\"r\", linestyle=\"--\")\n", + "plt.axvline(x=np.mean(errors_bad), color=\"y\", linestyle=\"--\")\n", + "plt.legend([\"zero error\", \"mean error\"])\n", + "\n", + "plt.show()" + ] } ], "metadata": { From e97eda302745745a0ff991589ef0dab0aa82429f Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Mon, 14 Jul 2025 09:29:35 -0400 Subject: [PATCH 17/22] fix TOC typo, add draft of graphical estimation section --- book/_toc.yml | 2 +- book/estimation/ecdf_kde.ipynb | 261 ++++++++++++++++++++++++++++++++- 2 files changed, 260 insertions(+), 3 deletions(-) diff --git a/book/_toc.yml b/book/_toc.yml index 6a6ef6f..5390a2b 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -19,7 +19,7 @@ parts: - file: estimation/avg_sd_correlation.ipynb - file: estimation/ecdf_kde.ipynb - file: estimation/misc/Central_Limit_Theorem.ipynb - - file: estimation/misc/Law_of_Large_Nums.ipynb + - file: estimation/misc/Law_Of_Large_Nums.ipynb - caption: Miscellaneous chapters: - file: references.md diff --git a/book/estimation/ecdf_kde.ipynb b/book/estimation/ecdf_kde.ipynb index d827def..81328ff 100644 --- a/book/estimation/ecdf_kde.ipynb +++ b/book/estimation/ecdf_kde.ipynb @@ -5,13 +5,270 @@ "id": "f44a373b", "metadata": {}, "source": [ - "# Histogram, Empirical Cumulative Distribution, Kernel Density Estimate" + "# Histogram, Empirical Cumulative Distribution, Kernel Density Estimate\n", + "\n", + "In this section we will be exploring these graphical estimates using simulation. We will start by sampling from the normal distribution and plotting a histogram, empirical cumulative distribution function (ECDF) and kernel density estimate (KDE).\n", + "\n", + "We will start by comparing a histogram of the sample to the actual distribution. Adjust the sample size to see the effect on the histogram." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49ac41ab", + "metadata": { + "tags": [ + "thebe-remove-input-init" + ] + }, + "outputs": [], + "source": [ + "import micropip\n", + "\n", + "await micropip.install(\"seaborn\")\n", + "await micropip.install(\"ipywidgets\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83af0ff4", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "from ipywidgets import interactive, fixed\n", + "from scipy.stats import norm, ecdf, expon\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b42e084", + "metadata": {}, + "outputs": [], + "source": [ + "def update_histogram(mu_val, scale_val, sample_size):\n", + " rng = np.random.default_rng()\n", + " data_norm = rng.normal(loc=mu_val, scale=scale_val, size=sample_size)\n", + " x = np.linspace(mu_val - 4 * scale_val, mu_val + 4 * scale_val, 1000)\n", + " plt.figure()\n", + " plt.plot(\n", + " x,\n", + " norm.pdf(x, loc=mu_val, scale=scale_val),\n", + " \"b-\",\n", + " label=\"Theoretical Normal Distribution\",\n", + " )\n", + " plt.hist(data_norm, density=True)\n", + " plt.title(\n", + " f\"Histogram of Normal Distribution $\\mu$ = {mu_val:.2f}, $\\sigma$ = {scale_val:.2f}\"\n", + " )\n", + " plt.xlabel(\"$X$\")\n", + " plt.ylabel(\"Frequency\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + "\n", + "mu_true = 1.0\n", + "scale_true = 0.5\n", + "slider_n = widgets.IntSlider(\n", + " value=50, min=10, max=200, step=10, description=\"Sample Size\"\n", + ")\n", + "interactive_plot = interactive(\n", + " update_histogram,\n", + " mu_val=fixed(mu_true),\n", + " scale_val=fixed(scale_true),\n", + " sample_size=slider_n,\n", + ")\n", + "interactive_plot" + ] + }, + { + "cell_type": "markdown", + "id": "3c81d9bb", + "metadata": {}, + "source": [ + "As we can see, the histogram becomes a better estimator for the true distribution when the sample size is larger. A similar effect can be observed in the ECDF. Run the code below to compare the sample ECDF and theoretical ECDF." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "514d9188", + "metadata": {}, + "outputs": [], + "source": [ + "def update_ecdf(mu_val, scale_val, sample_size):\n", + " rng = np.random.default_rng()\n", + " x = np.linspace(mu_val - 4 * scale_val, mu_val + 4 * scale_val, 1000)\n", + "\n", + " data_norm = rng.normal(loc=mu_val, scale=scale_val, size=sample_size)\n", + " ecdf_data = ecdf(data_norm)\n", + " ecdf_y = ecdf_data.cdf.evaluate(x)\n", + "\n", + " actual_cdf = norm.cdf(x, loc=mu_val, scale=scale_val)\n", + "\n", + " plt.figure()\n", + " plt.plot(x, actual_cdf, \"b-\", linewidth=2, label=\"Theoretical Normal CDF\")\n", + "\n", + " plt.plot(x, ecdf_y, \"r-\", label=\"Sample ECDF\")\n", + " plt.title(\n", + " f\"ECDF of Normal Distribution $\\mu$ = {mu_val:.2f}, $\\sigma$ = {scale_val:.2f}\"\n", + " )\n", + " plt.xlabel(\"$X$\")\n", + " plt.ylabel(\"Frequency\")\n", + " plt.legend()\n", + "\n", + "\n", + "mu_true = 1.0\n", + "scale_true = 0.5\n", + "slider_n = widgets.IntSlider(\n", + " value=50, min=10, max=200, step=10, description=\"Sample Size\"\n", + ")\n", + "interactive_plot = interactive(\n", + " update_ecdf,\n", + " mu_val=fixed(mu_true),\n", + " scale_val=fixed(scale_true),\n", + " sample_size=slider_n,\n", + ")\n", + "interactive_plot" + ] + }, + { + "cell_type": "markdown", + "id": "baced830", + "metadata": {}, + "source": [ + "Finally, we analyze the KDE for the sample and compare it to the theoretical distribution. Again, feel free to adjust the sample size to see the effects." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c23b77d8", + "metadata": {}, + "outputs": [], + "source": [ + "def update_kde(mu_val, scale_val, sample_size):\n", + " rng = np.random.default_rng()\n", + " x = np.linspace(mu_val - 4 * scale_val, mu_val + 4 * scale_val, 1000)\n", + "\n", + " data_norm = rng.normal(loc=mu_val, scale=scale_val, size=sample_size)\n", + "\n", + " plt.figure()\n", + " plt.hist(data_norm, density=True)\n", + " sns.kdeplot(data_norm, fill=True, label=\"Sample KDE\", color=\"skyblue\", alpha=0.7)\n", + " plt.plot(\n", + " x,\n", + " norm.pdf(x, loc=mu_val, scale=scale_val),\n", + " label=\"Theoretical Normal Distribution\",\n", + " )\n", + " plt.title(\n", + " f\"KDE of Normal Distribution $\\mu$ = {mu_val:.2f}, $\\sigma$ = {scale_val:.2f}\"\n", + " )\n", + " plt.xlabel(\"$X$\")\n", + " plt.ylabel(\"Density\")\n", + " plt.legend()\n", + "\n", + "\n", + "mu_true = 1.0\n", + "scale_true = 0.5\n", + "slider_n = widgets.IntSlider(\n", + " value=50, min=10, max=200, step=10, description=\"Sample Size\"\n", + ")\n", + "interactive_plot = interactive(\n", + " update_kde,\n", + " mu_val=fixed(mu_true),\n", + " scale_val=fixed(scale_true),\n", + " sample_size=slider_n,\n", + ")\n", + "interactive_plot" + ] + }, + { + "cell_type": "markdown", + "id": "4a4e9e86", + "metadata": {}, + "source": [ + "Now let's look at some other distributions. In the code below we will be using the exponential distribution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6864b552", + "metadata": {}, + "outputs": [], + "source": [ + "def update_exp(lambda_val, sample_size):\n", + " rng = np.random.default_rng()\n", + " x = np.linspace(0, 1 / lambda_val * 5, 1000)\n", + "\n", + " data_norm = rng.exponential(scale=1 / lambda_val, size=sample_size)\n", + " plt.figure(figsize=(21, 5))\n", + "\n", + " plt.subplot(1, 3, 1)\n", + " plt.hist(data_norm, density=True)\n", + " plt.plot(\n", + " x,\n", + " expon.pdf(x, scale=1 / lambda_val),\n", + " label=\"Theoretical Exponential Distribution\",\n", + " )\n", + " plt.title(f\"Histogram of Exponential Distribution $\\lambda$ = {lambda_val:.2f}\")\n", + " plt.xlabel(\"$X$\")\n", + " plt.ylabel(\"Frequency\")\n", + " plt.legend()\n", + "\n", + " plt.subplot(1, 3, 2)\n", + " ecdf_data = ecdf(data_norm)\n", + " ecdf_y = ecdf_data.cdf.evaluate(x)\n", + " actual_cdf = expon.cdf(x, scale=1 / lambda_val)\n", + " plt.plot(x, actual_cdf, \"b-\", linewidth=2, label=\"Theoretical Exponential CDF\")\n", + " plt.plot(x, ecdf_y, \"r-\", label=\"Sample ECDF\")\n", + " plt.title(f\"ECDF of Exponential Distribution $\\lambda$ = {lambda_val:.2f}\")\n", + " plt.xlabel(\"$X$\")\n", + " plt.ylabel(\"Frequency\")\n", + " plt.legend()\n", + "\n", + " plt.subplot(1, 3, 3)\n", + " plt.hist(data_norm, density=True)\n", + " plt.plot(\n", + " x,\n", + " expon.pdf(x, scale=1 / lambda_val),\n", + " label=\"Theoretical Exponential Distribution\",\n", + " )\n", + " sns.kdeplot(data_norm, fill=True, label=\"Sample KDE\", color=\"skyblue\", alpha=0.7)\n", + " plt.title(f\"KDE of Exponential Distribution $\\lambda$ = {lambda_val:.2f}\")\n", + " plt.xlabel(\"$X$\")\n", + " plt.ylabel(\"Density\")\n", + " plt.legend()\n", + "\n", + "\n", + "lambda_true = 0.5\n", + "slider_n = widgets.IntSlider(\n", + " value=50, min=10, max=200, step=10, description=\"Sample Size\"\n", + ")\n", + "interactive_plot = interactive(\n", + " update_exp,\n", + " lambda_val=fixed(lambda_true),\n", + " sample_size=slider_n,\n", + ")\n", + "interactive_plot" ] } ], "metadata": { + "kernelspec": { + "display_name": "prob_stat_book", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "name": "python", + "version": "3.13.5" } }, "nbformat": 4, From 1815ac0368753a19cbf0dfed081f15528c2919a8 Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Thu, 17 Jul 2025 16:01:47 -0400 Subject: [PATCH 18/22] switch to using generator for sampling from distributions to comply with most recent numpy version, add bias and mse computations for 911 section, add section discussing naive models at the end of birth months, improve MSE section with additional plots and refine text, add correlation to avg, sd, correlation section and polish other parts, add likelihood section plots to MLE section, add discrete distribution example to graphical estimation section, update all sections to add text highlighting the functions and libraries used where necessary, format all code, add miscellaneous section for loln and clt pages --- book/_toc.yml | 6 +- book/estimation/911_Calls_Estimation.ipynb | 45 ++- book/estimation/Bias.ipynb | 10 +- book/estimation/Birth_Month_Estimation.ipynb | 10 +- book/estimation/Confidence_Intervals.ipynb | 14 +- .../Maximum_Likelihood_Estimate.ipynb | 199 +++++++++--- book/estimation/Mean_Squared_Error.ipynb | 302 ++++++++++++------ book/estimation/avg_sd_correlation.ipynb | 104 +++++- book/estimation/ecdf_kde.ipynb | 156 ++++++++- book/estimation/misc/Law_of_Large_Nums.ipynb | 5 +- book/estimation/misc/overview.md | 7 + 11 files changed, 688 insertions(+), 170 deletions(-) create mode 100644 book/estimation/misc/overview.md diff --git a/book/_toc.yml b/book/_toc.yml index 5390a2b..38ce9f9 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -18,8 +18,10 @@ parts: - file: estimation/Confidence_Intervals.ipynb - file: estimation/avg_sd_correlation.ipynb - file: estimation/ecdf_kde.ipynb - - file: estimation/misc/Central_Limit_Theorem.ipynb - - file: estimation/misc/Law_Of_Large_Nums.ipynb + - file: estimation/misc/overview.md + sections: + - file: estimation/misc/Central_Limit_Theorem.ipynb + - file: estimation/misc/Law_Of_Large_Nums.ipynb - caption: Miscellaneous chapters: - file: references.md diff --git a/book/estimation/911_Calls_Estimation.ipynb b/book/estimation/911_Calls_Estimation.ipynb index b080bb6..70ca718 100644 --- a/book/estimation/911_Calls_Estimation.ipynb +++ b/book/estimation/911_Calls_Estimation.ipynb @@ -170,8 +170,49 @@ "source": [ "Given these two estimators, we need to determine the best one. $\\hat{p}$ is an unbiased estimator, but $e^{-\\hat{\\mu}}$ is positively biased. From the histograms, we also observe that $\\hat{p}$ has a larger variance than $e^{-\\hat{\\mu}}$. This translates to being typically far away from the true value versus being typically close to a value above the true value. We typically want to select the estimator with the lowest mean squared error (MSE).\n", "\n", - "Using the true value from the dataset we can estimate the MSE value for both estimators. Because $\\hat{p}$ is unbiased, the MSE for $\\hat{p}$ will be equal to the variance, which is $\\frac{p(1-p)}{n}$. The second estimator, $e^{-\\hat{\\mu}}$, is positively biased and can be written as a function of $p$ to be $(p^{n(1-e^{-\\frac{1}{n}})}-p)^2 + (p^{n(1-e^{-\\frac{2}{n}})}-p^{2n(1-e^{-\\frac{1}{n}})})$. \n", - "" + "Using the true value from the dataset we can estimate the MSE value for both estimators. Our true value will be the calculated by considering all phone calls taken between 6 and 7." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f707b53", + "metadata": {}, + "outputs": [], + "source": [ + "df_full = pd.read_csv(\"data/911_Calls.csv\")\n", + "df_full[\"datetime\"] = pd.to_datetime(df_full[\"Date\"] + \" \" + df_full[\"Call time\"])\n", + "df_full[\"date\"] = df_full[\"datetime\"].dt.date.astype(str)\n", + "df_full[\"hour\"] = df_full[\"datetime\"].dt.hour\n", + "df_full[\"minute\"] = df_full[\"datetime\"].dt.minute\n", + "df_hour = df_full[df_full[\"hour\"] == 6]\n", + "counts = df_hour.groupby([\"date\", \"minute\"]).size().reset_index(name=\"count\")\n", + "\n", + "hourly_counts = {}\n", + "for date, grp in counts.groupby(\"date\"):\n", + " series = grp.set_index(\"minute\")[\"count\"]\n", + " full = series.reindex(range(60), fill_value=0)\n", + " hourly_counts[date] = full.tolist()\n", + "\n", + "total_zero_minutes = np.sum([counts.count(0) for counts in hourly_counts.values()])\n", + "total_minutes = len(hourly_counts) * 60\n", + "actual_prop = total_zero_minutes / total_minutes\n", + "\n", + "mse_prop = np.sum((actual_prop - p_hat) ** 2) / len(p_hat)\n", + "bias_prop = np.mean(p_hat) - actual_prop\n", + "\n", + "mse_mle = np.sum((actual_prop - mle) ** 2) / len(mle)\n", + "bias_mle = np.mean(mle) - actual_prop\n", + "\n", + "print(f\"Mean Squared Error for proportion: {mse_prop}\")\n", + "print(f\"Mean Squared Error for MLE: {mse_mle}\")\n", + "\n", + "print(f\"Bias for proportion: {bias_prop}\")\n", + "print(f\"Bias for MLE: {bias_mle}\")\n", + "\n", + "\n", + "print(f\"MSE - Bias^2 for proportion: {mse_prop - bias_prop ** 2}\")\n", + "print(f\"MSE - Bias^2 for MLE: {mse_mle - bias_mle ** 2}\")" ] } ], diff --git a/book/estimation/Bias.ipynb b/book/estimation/Bias.ipynb index 875c349..1dfc409 100644 --- a/book/estimation/Bias.ipynb +++ b/book/estimation/Bias.ipynb @@ -15,7 +15,7 @@ "\n", "To illustrate the impact of bias on these estimators, we can simulate the estimates on many samples of data and visualise the results in a histogram.\n", "\n", - "First, we simulate the exponential distribution." + "First, we simulate sampling the exponential distribution using numpy's `random.Generator.exponential` function. " ] }, { @@ -42,7 +42,8 @@ "\n", " # simulate samples\n", " for i in range(samples):\n", - " sample = np.random.exponential(scale=1 / lambda_true, size=sample_size)\n", + " rng = np.random.default_rng()\n", + " sample = rng.exponential(1 / lambda_true, sample_size)\n", " if i == 0:\n", " running_avg.append(sample_size / sample.sum())\n", " else:\n", @@ -91,7 +92,7 @@ "id": "5c148cda", "metadata": {}, "source": [ - "Next we simulate the normal distribution." + "Next we simulate the normal distribution using `random.Generator.normal`." ] }, { @@ -107,7 +108,8 @@ "\n", " # simulate samples\n", " for i in range(samples):\n", - " sample = np.random.normal(loc=mu_true, scale=1.0, size=sample_size)\n", + " rng = np.random.default_rng()\n", + " sample = rng.normal(loc=mu_true, scale=1.0, size=sample_size)\n", " if i == 0:\n", " running_avg.append(sample.sum() / sample_size)\n", " else:\n", diff --git a/book/estimation/Birth_Month_Estimation.ipynb b/book/estimation/Birth_Month_Estimation.ipynb index b9cea32..81bb6a0 100644 --- a/book/estimation/Birth_Month_Estimation.ipynb +++ b/book/estimation/Birth_Month_Estimation.ipynb @@ -39,7 +39,7 @@ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import ipywidgets as widgets\n", - "from ipywidgets import interactive, fixed\n", + "from ipywidgets import interactive\n", "\n", "months = [\n", " \"January\",\n", @@ -205,6 +205,14 @@ "interactive_plot" ] }, + { + "cell_type": "markdown", + "id": "7165b89f", + "metadata": {}, + "source": [ + "The plots above illustrate that models can be constructed based on reasoning but the data may not entirely corroborate them. When creating a model, we can base this model off of data or off of reasoning, but it is important to check that the model is an accurate representation of the actual data." + ] + }, { "cell_type": "markdown", "id": "e1851d05", diff --git a/book/estimation/Confidence_Intervals.ipynb b/book/estimation/Confidence_Intervals.ipynb index b4cc548..ec9dda6 100644 --- a/book/estimation/Confidence_Intervals.ipynb +++ b/book/estimation/Confidence_Intervals.ipynb @@ -26,6 +26,14 @@ "```" ] }, + { + "cell_type": "markdown", + "id": "db122c9f", + "metadata": {}, + "source": [ + "In the code below we use scipy's `stats.t.ppf` to calculate the t-statistic and numpy's `std` function with `ddof=1` to calculate sample standard deviation." + ] + }, { "cell_type": "code", "execution_count": null, @@ -73,7 +81,8 @@ "confidence_level = 0.95\n", "\n", "for x in x_values:\n", - " sample = np.random.normal(loc=expectation, scale=standard_deviation, size=n)\n", + " rng = np.random.default_rng()\n", + " sample = rng.normal(loc=expectation, scale=standard_deviation, size=n)\n", "\n", " point_estimate = np.mean(sample)\n", " sample_std = np.std(sample, ddof=1)\n", @@ -366,7 +375,7 @@ "\n", "$$p_0 = \\frac{\\hat{p} + \\frac{z^2}{2n} \\pm z\\sqrt{\\frac{\\hat{p}(1-\\hat{p})}{n} + \\frac{z^2}{4n^2}}}{1 + \\frac{z^2}{n}} $$\n", "\n", - "In both of these cases, the confidence level is only approximate, and the Wilson method achieves on average a better approximation. The code below simulates confidence intervals calculated using both methods." + "In both of these cases, the confidence level is only approximate, and the Wilson method achieves on average a better approximation. The code below simulates confidence intervals calculated using both methods. We use scipy's `stats.norm.ppf` to get the z-score value." ] }, { @@ -489,7 +498,6 @@ "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 15))\n", "\n", "sample_indices = np.arange(1, num_samples + 1)\n", - "colors = plt.cm.tab20(np.linspace(0, 1, num_samples))\n", "\n", "for i in range(num_samples):\n", " contains_true = naive_lower[i] <= p_true <= naive_upper[i]\n", diff --git a/book/estimation/Maximum_Likelihood_Estimate.ipynb b/book/estimation/Maximum_Likelihood_Estimate.ipynb index 7565265..64c6a3a 100644 --- a/book/estimation/Maximum_Likelihood_Estimate.ipynb +++ b/book/estimation/Maximum_Likelihood_Estimate.ipynb @@ -38,7 +38,7 @@ "\\end{aligned}\n", "$$\n", "\n", - "We differentiate the log-likelihood with respect to $\\lambda$.\n", + "\n", "\n", - "So we arrive at $\\hat{\\lambda} = \\frac{n}{\\sum_{i=1}^n x_i}$." + "Using the likelihood function above, we arrive at $\\hat{\\lambda} = \\frac{n}{\\sum_{i=1}^n x_i}$." ] }, { @@ -85,7 +85,7 @@ "\\end{aligned}\n", "$$\n", "\n", - "We differentiate the log-likelihood with respect to $\\mu$ and $\\sigma$.\n", + "\n", "\n", - "Now we set the result to $0$ to calculate $\\hat{\\mu}$ and $\\hat{\\sigma}$.\n", + "\n", "\n", - "$$\n", + "\n", + "\n", + "We arrive at $\\hat{\\mu} = \\frac 1 n \\sum_{i=1}^n x_i$ and $\\hat{\\sigma} = \\sqrt{\\frac{\\sum_{i=1}^n (x_i - \\mu)^2}{n}}$." ] }, { @@ -129,7 +131,7 @@ "id": "8f8bd062", "metadata": {}, "source": [ - "Now that we have calculated these estimators, let's confirm that they accurately estimate where the likelihood is maximized. To do so, we can graph the likelihood function of each and the corresponding MLE." + "Now that we have calculated these estimators, let's confirm that they accurately estimate where the likelihood is maximized. To do so, we can graph the likelihood and log-likelihood functions of each and the corresponding MLE." ] }, { @@ -170,34 +172,55 @@ "source": [ "def update_plot(sample_size, lambda_val):\n", " rng = np.random.default_rng()\n", - " data_exp = rng.exponential(scale=1/lambda_val, size=sample_size)\n", + " data_exp = rng.exponential(scale=1 / lambda_val, size=sample_size)\n", "\n", " lambdas = np.linspace(0.01, 1.5, 500)\n", - "\n", - " # log likelihood for lambda\n", " n = data_exp.size\n", " sum_x = data_exp.sum()\n", - " logL = n * np.log(lambdas) - lambdas * sum_x\n", "\n", + " # likelihood for lambda\n", + " likelihood = (lambdas**n) * np.exp(-lambdas * sum_x)\n", + "\n", + " # log likelihood for lambda\n", + " logL = n * np.log(lambdas) - lambdas * sum_x\n", "\n", - " plt.figure()\n", - " plt.plot(lambdas, logL, lw=2)\n", + " plt.figure(figsize=(15, 6))\n", + " plt.subplot(1, 2, 1)\n", + " plt.plot(lambdas, likelihood)\n", " plt.axvline(n / sum_x, color=\"C1\", ls=\"--\", label=f\"MLE $\\lambda$={n/sum_x:.2f}\")\n", - " plt.axvline(lambda_val, color=\"tab:olive\", ls=\"--\", label=f\"True value $\\lambda$={lambda_val:.2f}\")\n", + " plt.axvline(\n", + " lambda_val,\n", + " color=\"tab:olive\",\n", + " ls=\"--\",\n", + " label=f\"True value $\\lambda$={lambda_val:.2f}\",\n", + " )\n", " plt.title(\"Exponential Likelihood\")\n", " plt.xlabel(\"$\\lambda$\")\n", " plt.ylabel(\"$L(\\lambda)$\")\n", " plt.legend()\n", + "\n", + " plt.subplot(1, 2, 2)\n", + " plt.plot(lambdas, logL)\n", + " plt.axvline(n / sum_x, color=\"C1\", ls=\"--\", label=f\"MLE $\\lambda$={n/sum_x:.2f}\")\n", + " plt.axvline(\n", + " lambda_val,\n", + " color=\"tab:olive\",\n", + " ls=\"--\",\n", + " label=f\"True value $\\lambda$={lambda_val:.2f}\",\n", + " )\n", + " plt.title(\"Exponential Log-Likelihood\")\n", + " plt.xlabel(\"$\\lambda$\")\n", + " plt.ylabel(\"$ln L(\\lambda)$\")\n", + " plt.legend()\n", " plt.show()\n", "\n", + "\n", "lambda_true = 0.5\n", "slider_n = widgets.IntSlider(\n", " value=50, min=10, max=200, step=10, description=\"Sample Size\"\n", ")\n", "interactive_plot = interactive(\n", - " update_plot,\n", - " sample_size=slider_n,\n", - " \tlambda_val=fixed(lambda_true)\n", + " update_plot, sample_size=slider_n, lambda_val=fixed(lambda_true)\n", ")\n", "interactive_plot" ] @@ -213,15 +236,38 @@ " rng = np.random.default_rng()\n", " data_norm = rng.normal(loc=mu_val, scale=2.0, size=n)\n", " mu = np.linspace(-2, 5, 200)\n", + " n = data_norm.size\n", + " sigma0 = data_norm.std(ddof=1)\n", + "\n", + " likelihood = []\n", + " logL_mu = []\n", "\n", - " # log likelihood for mu\n", - " sigma0 = data_norm.std(ddof=0)\n", - " logL_mu = [\n", - " (-len(data_norm) * np.log(sigma0) - np.sum((data_norm - m) ** 2) / (2 * sigma0**2))\n", - " for m in mu\n", - " ]\n", + " for m in mu:\n", + " log_likelihood = -n * np.log(sigma0 * np.sqrt(2 * np.pi)) - np.sum(\n", + " (data_norm - m) ** 2\n", + " ) / (2 * sigma0**2)\n", + " logL_mu.append(log_likelihood)\n", + "\n", + " likelihood.append(np.exp(log_likelihood))\n", + "\n", + " likelihood = np.array(likelihood)\n", + " logL_mu = np.array(logL_mu)\n", + "\n", + " plt.figure(figsize=(15, 6))\n", + " plt.subplot(1, 2, 1)\n", + " plt.plot(mu, likelihood)\n", + " plt.axvline(\n", + " data_norm.mean(), color=\"r\", ls=\"--\", label=f\"MLE $\\mu$={data_norm.mean():.2f}\"\n", + " )\n", + " plt.axvline(\n", + " mu_val, color=\"tab:olive\", ls=\"--\", label=f\"True value $\\mu$={mu_val:.2f}\"\n", + " )\n", + " plt.title(\"Likelihood vs $\\mu$\")\n", + " plt.xlabel(\"$\\mu$\")\n", + " plt.ylabel(\"$L(\\mu)$\")\n", + " plt.legend()\n", "\n", - " plt.figure()\n", + " plt.subplot(1, 2, 2)\n", " plt.plot(mu, logL_mu)\n", " plt.axvline(\n", " data_norm.mean(), color=\"r\", ls=\"--\", label=f\"MLE $\\mu$={data_norm.mean():.2f}\"\n", @@ -235,15 +281,12 @@ " plt.legend()\n", " plt.show()\n", "\n", + "\n", "mu_true = 1.0\n", "slider_n = widgets.IntSlider(\n", " value=50, min=10, max=200, step=10, description=\"Sample Size\"\n", ")\n", - "interactive_plot = interactive(\n", - " update_plot_mu,\n", - " n=slider_n,\n", - " \tmu_val=fixed(mu_true)\n", - ")\n", + "interactive_plot = interactive(update_plot_mu, n=slider_n, mu_val=fixed(mu_true))\n", "interactive_plot" ] }, @@ -259,14 +302,45 @@ " data_norm = rng.normal(loc=1.0, scale=sigma_val, size=n)\n", " sigma = np.linspace(0.1, 5, 200)\n", "\n", - " # log likelihood for sigma\n", " mu0 = data_norm.mean()\n", - " logL_sigma = [\n", - " -len(data_norm) * np.log(s) - np.sum((data_norm - mu0) ** 2) / (2 * s**2)\n", - " for s in sigma\n", - " ]\n", + " likelihood = []\n", + " logL_sigma = []\n", + "\n", + " sum_squared_deviations = np.sum((data_norm - mu0) ** 2)\n", + "\n", + " for s in sigma:\n", + " log_likelihood = (\n", + " -n / 2 * np.log(2 * np.pi)\n", + " - n / 2 * np.log(s**2)\n", + " - sum_squared_deviations / (2 * s**2)\n", + " )\n", + " logL_sigma.append(log_likelihood)\n", + "\n", + " likelihood.append(np.exp(log_likelihood))\n", + "\n", + " likelihood = np.array(likelihood)\n", + " logL_sigma = np.array(logL_sigma)\n", "\n", - " plt.figure()\n", + " plt.figure(figsize=(15, 6))\n", + " plt.subplot(1, 2, 1)\n", + " plt.plot(sigma, likelihood)\n", + " plt.axvline(\n", + " np.sqrt(np.sum((data_norm - mu0) ** 2) / len(data_norm)),\n", + " color=\"r\",\n", + " ls=\"--\",\n", + " label=f\"MLE $\\sigma$={np.sqrt(((data_norm-mu0)**2).mean()):.2f}\",\n", + " )\n", + " plt.axvline(\n", + " sigma_val,\n", + " color=\"tab:olive\",\n", + " ls=\"--\",\n", + " label=f\"True value $\\sigma$={sigma_val:.2f}\",\n", + " )\n", + " plt.title(\"Likelihood vs $\\sigma$\")\n", + " plt.xlabel(\"$\\sigma$\")\n", + " plt.ylabel(\"$L(\\sigma)$\")\n", + " plt.legend()\n", + " plt.subplot(1, 2, 2)\n", " plt.plot(sigma, logL_sigma)\n", " plt.axvline(\n", " np.sqrt(np.sum((data_norm - mu0) ** 2) / len(data_norm)),\n", @@ -286,14 +360,13 @@ " plt.legend()\n", " plt.show()\n", "\n", + "\n", "sigma_true = 2.0\n", "slider_n = widgets.IntSlider(\n", " value=50, min=10, max=200, step=10, description=\"Sample Size\"\n", ")\n", "interactive_plot = interactive(\n", - " update_plot_sigma,\n", - " n=slider_n,\n", - " \tsigma_val=fixed(sigma_true)\n", + " update_plot_sigma, n=slider_n, sigma_val=fixed(sigma_true)\n", ")\n", "interactive_plot" ] @@ -336,35 +409,57 @@ "outputs": [], "source": [ "def update_plot_exp(sample_size, lambda_val):\n", - " rng = np.random.default_rng()\n", - " data_exp = rng.exponential(scale=1/lambda_val, size=sample_size)\n", "\n", - " exp = np.linspace(0.01, 2.5, 500)\n", + " rng = np.random.default_rng()\n", + " data_exp = rng.exponential(scale=1 / lambda_val, size=sample_size)\n", "\n", - " # log likelihood for expectation of exponential distribution\n", + " lambdas = np.linspace(0.1, 10, 500)\n", " n = data_exp.size\n", " sum_x = data_exp.sum()\n", - " logL = -n * np.log(exp) - sum_x / exp\n", "\n", + " # likelihood for lambda\n", + " likelihood = ((1.0 / lambdas) ** n) * np.exp(-sum_x / lambdas)\n", "\n", - " plt.figure()\n", - " plt.plot(exp, logL, lw=2)\n", - " plt.axvline(sum_x/n, color=\"C1\", ls=\"--\", label=f\"MLE $E[X]$={sum_x/n:.2f}\")\n", - " plt.axvline(1/lambda_val, color=\"tab:olive\", ls=\"--\", label=f\"True value $E[X]$={1/lambda_val:.2f}\")\n", + " # log likelihood for lambda\n", + " logL = -n * np.log(lambdas) - sum_x / lambdas\n", + "\n", + " plt.figure(figsize=(15, 6))\n", + " plt.subplot(1, 2, 1)\n", + " plt.plot(lambdas, likelihood)\n", + " plt.axvline(sum_x / n, color=\"C1\", ls=\"--\", label=f\"MLE $\\lambda$={sum_x/n:.2f}\")\n", + " plt.axvline(\n", + " 1.0 / lambda_val,\n", + " color=\"tab:olive\",\n", + " ls=\"--\",\n", + " label=f\"True value $\\lambda$={1.0/lambda_val:.2f}\",\n", + " )\n", " plt.title(\"Exponential Likelihood\")\n", " plt.xlabel(\"$E[X]$\")\n", " plt.ylabel(\"$L(E[X])$\")\n", " plt.legend()\n", + "\n", + " plt.subplot(1, 2, 2)\n", + " plt.plot(lambdas, logL)\n", + " plt.axvline(sum_x / n, color=\"C1\", ls=\"--\", label=f\"MLE $\\lambda$={sum_x/n:.2f}\")\n", + " plt.axvline(\n", + " 1.0 / lambda_val,\n", + " color=\"tab:olive\",\n", + " ls=\"--\",\n", + " label=f\"True value $\\lambda$={1.0/lambda_val:.2f}\",\n", + " )\n", + " plt.title(\"Exponential Log-Likelihood\")\n", + " plt.xlabel(\"$E[X]$\")\n", + " plt.ylabel(\"$ln L(E[X])$\")\n", + " plt.legend()\n", " plt.show()\n", "\n", + "\n", "lambda_true = 0.5\n", "slider_n = widgets.IntSlider(\n", " value=50, min=10, max=200, step=10, description=\"Sample Size\"\n", ")\n", "interactive_plot = interactive(\n", - " update_plot_exp,\n", - " sample_size=slider_n,\n", - " \tlambda_val=fixed(lambda_true)\n", + " update_plot_exp, sample_size=slider_n, lambda_val=fixed(lambda_true)\n", ")\n", "interactive_plot" ] diff --git a/book/estimation/Mean_Squared_Error.ipynb b/book/estimation/Mean_Squared_Error.ipynb index 87cab73..f8561eb 100644 --- a/book/estimation/Mean_Squared_Error.ipynb +++ b/book/estimation/Mean_Squared_Error.ipynb @@ -7,9 +7,9 @@ "source": [ "# Mean Squared Error\n", "\n", - "In this section we will be exploring mean squared error (MSE). We will be looking at the MSE of a simulated exponential and normal distribution. \n", + "In this section we will be exploring mean squared error (MSE). We will be looking at the MSE of a simulated exponential and normal distribution. In order to sample from these distributions, we will use numpy's `random.Generator.exponential` and `random.Generator.normal` functions.\n", "\n", - "We will start by simulating the exponential distribution to estimate $\\lambda$ and plotting histograms of the estimates as well as the errors in the simulation." + "We will start by simulating the exponential distribution to estimate $\\lambda$. Run the code blocks below to simulate sampling many times from the exponential distribution. We will use the maximum likelihood estimator to estimate $\\lambda$ and plot histograms of the estimates. We also compute the error of the estimates by calculating the difference between each estimate and the true $\\lambda$ value. Finally, we calculate the MSE." ] }, { @@ -57,11 +57,12 @@ "\n", "# simulate samples\n", "for i in range(samples):\n", - " sample = np.random.exponential(scale=1 / lambda_true, size=sample_size)\n", + " rng = np.random.default_rng()\n", + " sample = rng.exponential(scale=1 / lambda_true, size=sample_size)\n", "\n", " mle = sample_size / sample.sum()\n", " estimates.append(mle)\n", - " error = mle - lambda_true\n", + " error = lambda_true - mle\n", " errors.append(error)\n", "\n", "estimates = np.array(estimates)\n", @@ -89,9 +90,59 @@ "plt.ylabel(\"frequency\")\n", "plt.xlabel(\"Error (estimate - true value)\")\n", "\n", - "plt.axvline(x=0, color=\"r\", linestyle=\"--\")\n", - "plt.axvline(x=np.mean(errors), color=\"y\", linestyle=\"--\")\n", - "plt.legend([\"zero error\", \"mean error\"])\n", + "plt.axvline(x=mse, color=\"y\", linestyle=\"--\")\n", + "plt.legend([\"mse\"])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d8985b2c", + "metadata": {}, + "source": [ + "Now lets investigate the impact of $\\lambda$ on the MSE. In the code block below we plot the calculated MSE versus the value of $\\lambda$. Observe the relationship between $\\lambda$ and the MSE." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f7d2bca", + "metadata": {}, + "outputs": [], + "source": [ + "def simulate_exponential(lambda_vals, sample_size=100, samples=1000):\n", + " mse = []\n", + " # simulate samples\n", + " for i in lambda_vals:\n", + " errors = []\n", + " for j in range(samples):\n", + " rng = np.random.default_rng()\n", + " sample = rng.exponential(scale=1 / i, size=sample_size)\n", + "\n", + " mle = sample_size / sample.sum()\n", + " error = i - mle\n", + " errors.append(error)\n", + "\n", + " errors = np.array(errors)\n", + " mse.append(np.mean(errors**2))\n", + "\n", + " return mse\n", + "\n", + "\n", + "lambda_vals = np.linspace(0.1, 2, 150)\n", + "sample_size = 100\n", + "samples = 1000\n", + "mse_vals = simulate_exponential(lambda_vals, sample_size, samples)\n", + "\n", + "plt.figure(figsize=(14, 8))\n", + "\n", + "\n", + "plt.plot(lambda_vals, mse_vals, label=\"mse values\")\n", + "plt.title(\"Mean Squared Error vs Lambda Values\")\n", + "plt.legend()\n", + "plt.xlabel(\"Lambda Values\")\n", + "plt.ylabel(\"MSE\")\n", "\n", "plt.show()" ] @@ -101,7 +152,9 @@ "id": "bcdd22f3", "metadata": {}, "source": [ - "Next, we turn our attention to the normal distribution. " + "Next, we turn our attention to the normal distribution. \n", + "\n", + "We will repeat the same thing we did with the exponential distribution but this time to estimate $\\mu$ in the normal distribution. Run the code block below to simulate sampling many times from the normal distribution. We will again use the maximum likelihood estimator to estimate $\\mu$ and plot histograms of the estimates. We compute the error of the estimates by calculating the difference between each estimate and the true $\\mu$ value and calculate the MSE." ] }, { @@ -120,11 +173,12 @@ "\n", "# simulate samples\n", "for i in range(samples):\n", - " sample = np.random.normal(loc=mu_true, scale=1.0, size=sample_size)\n", + " rng = np.random.default_rng()\n", + " sample = rng.normal(loc=mu_true, scale=1.0, size=sample_size)\n", "\n", " mle = sample.sum() / sample_size\n", " estimates.append(mle)\n", - " error = mle - mu_true\n", + " error = mu_true - mle\n", " errors.append(error)\n", "\n", "estimates = np.array(estimates)\n", @@ -152,9 +206,59 @@ "plt.ylabel(\"frequency\")\n", "plt.xlabel(\"Error (estimate - true value)\")\n", "\n", - "plt.axvline(x=0, color=\"r\", linestyle=\"--\")\n", - "plt.axvline(x=np.mean(errors), color=\"y\", linestyle=\"--\")\n", - "plt.legend([\"zero error\", \"mean error\"])\n", + "plt.axvline(x=mse, color=\"y\", linestyle=\"--\")\n", + "plt.legend([\"mse\"])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ac4b7d74", + "metadata": {}, + "source": [ + "Again, we investigate the impact of the value of $\\mu$ on the MSE. Run the code below to plot the MSE versus the value of $\\mu$ and observe the relationship between the two values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f52944f", + "metadata": {}, + "outputs": [], + "source": [ + "def simulate_normal(mu_vals, sample_size=100, samples=1000):\n", + " mse = []\n", + " # simulate samples\n", + " for i in mu_vals:\n", + " errors = []\n", + " for j in range(samples):\n", + " rng = np.random.default_rng()\n", + " sample = rng.normal(loc=i, scale=1.0, size=sample_size)\n", + "\n", + " mle = sample.sum() / sample_size\n", + " error = i - mle\n", + " errors.append(error)\n", + "\n", + " errors = np.array(errors)\n", + " mse.append(np.mean(errors**2))\n", + "\n", + " return mse\n", + "\n", + "\n", + "mu_vals = np.linspace(0, 2, 150)\n", + "sample_size = 100\n", + "samples = 1000\n", + "mse_vals = simulate_normal(mu_vals, sample_size, samples)\n", + "\n", + "plt.figure(figsize=(14, 8))\n", + "\n", + "\n", + "plt.plot(lambda_vals, mse_vals, label=\"mse values\")\n", + "plt.title(\"Mean Squared Error vs Mu Values\")\n", + "plt.legend()\n", + "plt.xlabel(\"Mu Values\")\n", + "plt.ylabel(\"MSE\")\n", "\n", "plt.show()" ] @@ -164,118 +268,128 @@ "id": "77e5625c", "metadata": {}, "source": [ - "Let us now consider the bad example where we use the minimum of a sample times the sample size to estimate the expectation of the exponential distribution. We compare this estimate to that of the maximum likelihood estimator in the code below." + "Let us now consider the example where we use the minimum of a sample times the sample size to estimate the expectation of the exponential distribution. Run the code below to plot histograms of the estimates and the associated error values alongside the MSE. Use the slider to adjust the value of $\\lambda$." ] }, { "cell_type": "code", "execution_count": null, - "id": "b20e8bef", + "id": "890864fd", "metadata": {}, "outputs": [], "source": [ - "def update_plot_exp(sample_size, lambda_val):\n", - " rng = np.random.default_rng()\n", - " data_exp = rng.exponential(scale=1/lambda_val, size=sample_size)\n", - " min_val = data_exp.min()\n", - " bad_estimate = min_val * sample_size\n", - "\n", - " exp = np.linspace(0.01, 2.5, 500)\n", - "\n", - " n = data_exp.size\n", - " sum_x = data_exp.sum()\n", - " logL = -n * np.log(exp) - sum_x / exp\n", - "\n", - "\n", - " plt.figure()\n", - " plt.plot(exp, logL, lw=2)\n", - " plt.axvline(sum_x/n, color=\"C1\", ls=\"--\", label=f\"MLE $E[X]$={sum_x/n:.2f}\")\n", - " plt.axvline(1/lambda_val, color=\"tab:olive\", ls=\"--\", label=f\"True value $E[X]$={1/lambda_val:.2f}\")\n", - " plt.axvline(bad_estimate, color=\"tab:green\", ls=\"--\", label=f\"minimum x sample size $E[X]$={bad_estimate:.2f}\")\n", - " plt.title(\"Exponential Likelihood\")\n", - " plt.xlabel(\"$E[X]$\")\n", - " plt.ylabel(\"$L(E[X])$\")\n", - " plt.legend()\n", + "def update_plot(lambda_value):\n", + " estimates = []\n", + " errors = []\n", + "\n", + " # simulate samples\n", + " for i in range(samples):\n", + " rng = np.random.default_rng()\n", + " sample = rng.exponential(scale=1.0 / lambda_value, size=sample_size)\n", + "\n", + " estimate = sample.min() * sample_size\n", + " estimates.append(estimate)\n", + " error = (1.0 / lambda_value) - estimate\n", + " errors.append(error)\n", + "\n", + " estimates = np.array(estimates)\n", + " mean_estimates = estimates.mean()\n", + " errors = np.array(errors)\n", + " mse = np.mean(errors**2)\n", + "\n", + " print(f\"MSE = {mse:.4f}\")\n", + "\n", + " # plot estimates\n", + " plt.figure(figsize=(15, 6))\n", + " plt.subplot(1, 2, 1)\n", + " plt.hist(estimates)\n", + " plt.title(\"Estimation $1 / \\hat{\\lambda}$\")\n", + " plt.ylabel(\"frequency\")\n", + " plt.xlabel(\"$1 / \\lambda$ estimates\")\n", + "\n", + " plt.axvline(x=1.0 / lambda_value, color=\"r\", linestyle=\"--\")\n", + " plt.axvline(x=mean_estimates, color=\"y\", linestyle=\"--\")\n", + " plt.legend([\"true 1/$\\lambda$ value\", \"mean of estimates\"])\n", + "\n", + " plt.subplot(1, 2, 2)\n", + " plt.hist(errors)\n", + " plt.title(\"Errors minimum x sample size\")\n", + " plt.ylabel(\"frequency\")\n", + " plt.xlabel(\"Error\")\n", + "\n", + " plt.axvline(x=mse, color=\"y\", linestyle=\"--\")\n", + " plt.legend([\"mse\"])\n", + "\n", " plt.show()\n", "\n", - "lambda_true = 0.5\n", - "slider_n = widgets.IntSlider(\n", - " value=50, min=10, max=200, step=10, description=\"Sample Size\"\n", - ")\n", - "interactive_plot = interactive(\n", - " update_plot_exp,\n", - " sample_size=slider_n,\n", - " \tlambda_val=fixed(lambda_true)\n", + "\n", + "lambda_true = widgets.FloatSlider(\n", + " value=0.5, min=0.1, max=5, step=0.1, description=\"Lambda\"\n", ")\n", + "sample_size = 100\n", + "samples = 1000\n", + "interactive_plot = interactive(update_plot, lambda_value=lambda_true)\n", "interactive_plot" ] }, + { + "cell_type": "markdown", + "id": "378e3ec9", + "metadata": {}, + "source": [ + "Run the code below to plot the MSE versus the value of $\\lambda$ to observe the relationship between the two." + ] + }, { "cell_type": "code", "execution_count": null, - "id": "890864fd", + "id": "8ce8faf6", "metadata": {}, "outputs": [], "source": [ - "lambda_true = 0.5\n", - "sample_size = 100\n", - "samples = 1000\n", - "\n", - "estimates = []\n", - "errors = []\n", - "bad_estimates = []\n", - "errors_bad = []\n", - "\n", - "# simulate samples\n", - "for i in range(samples):\n", - " sample = np.random.exponential(scale=1 / lambda_true, size=sample_size)\n", + "def simulate_exponential_bad(lambda_vals, sample_size=100, samples=1000):\n", + " mse = []\n", + " # simulate samples\n", + " for i in lambda_vals:\n", + " errors = []\n", + " for j in range(samples):\n", + " rng = np.random.default_rng()\n", + " sample = rng.exponential(scale=1 / i, size=sample_size)\n", "\n", - " mle = sample_size / sample.sum()\n", - " estimates.append(mle)\n", - " error = mle - lambda_true\n", - " errors.append(error)\n", + " mle = sample.min() * sample_size\n", + " error = (1.0 / i) - mle\n", + " errors.append(error)\n", "\n", - " bad_estimate = sample.min() * sample_size\n", - " bad_estimates.append(bad_estimate)\n", - " error_bad = bad_estimate - lambda_true\n", - " errors_bad.append(error_bad)\n", + " errors = np.array(errors)\n", + " mse.append(np.mean(errors**2))\n", "\n", - "estimates = np.array(estimates)\n", - "mean_estimates = estimates.mean()\n", - "errors = np.array(errors)\n", - "mse = np.mean(errors**2)\n", - "bad_estimates = np.array(bad_estimates)\n", - "bad_mean_estimate = bad_estimates.mean()\n", - "errors_bad = np.array(errors_bad)\n", - "mse_bad = np.mean(errors_bad**2)\n", + " return mse\n", "\n", - "print(f\"MSE = {mse:.4f}\")\n", - "print(f\"MSE bad = {mse_bad:.4f}\")\n", "\n", - "# plot estimates\n", - "plt.figure(figsize=(15, 6))\n", - "plt.subplot(1, 2, 1)\n", - "plt.hist(errors)\n", - "plt.title(\"Errors MSE\")\n", - "plt.ylabel(\"frequency\")\n", - "plt.xlabel(\"Error (estimate - true value)\")\n", + "lambda_vals = np.linspace(0.1, 2, 150)\n", + "sample_size = 100\n", + "samples = 1000\n", + "mse_vals = simulate_exponential_bad(lambda_vals, sample_size, samples)\n", "\n", - "plt.axvline(x=0, color=\"r\", linestyle=\"--\")\n", - "plt.axvline(x=np.mean(mse), color=\"y\", linestyle=\"--\")\n", - "plt.legend([\"zero error\", \"mean error\"])\n", + "plt.figure(figsize=(14, 8))\n", "\n", - "plt.subplot(1, 2, 2)\n", - "plt.hist(errors_bad)\n", - "plt.title(\"Errors minimum x sample size\")\n", - "plt.ylabel(\"frequency\")\n", - "plt.xlabel(\"Error (bad estimate - true value)\")\n", "\n", - "plt.axvline(x=0, color=\"r\", linestyle=\"--\")\n", - "plt.axvline(x=np.mean(errors_bad), color=\"y\", linestyle=\"--\")\n", - "plt.legend([\"zero error\", \"mean error\"])\n", + "plt.plot(lambda_vals, mse_vals, label=\"mse values\")\n", + "plt.title(\"Mean Squared Error vs Lambda Values (estimating $1/\\lambda$)\")\n", + "plt.legend()\n", + "plt.xlabel(\"Lambda Values\")\n", + "plt.ylabel(\"MSE\")\n", "\n", "plt.show()" ] + }, + { + "cell_type": "markdown", + "id": "9032b008", + "metadata": {}, + "source": [ + "As we can observe from these examples, the MSE value is related to the width of the errors histogram. Narrow histograms have a smaller MSE while wider histograms typically have a larger MSE. Additionally, the value of MSE relates to bias. More bias can result in a higher MSE." + ] } ], "metadata": { diff --git a/book/estimation/avg_sd_correlation.ipynb b/book/estimation/avg_sd_correlation.ipynb index e4007bc..fe4fce2 100644 --- a/book/estimation/avg_sd_correlation.ipynb +++ b/book/estimation/avg_sd_correlation.ipynb @@ -9,7 +9,7 @@ "\n", "In this section we will be exploring estimators for the expectation, standard deviation and correlation and how accurate they are.\n", "\n", - "Let's start by looking at the normal distribution. In the code below we calculate the sample average and sample standard deviation and compare it to the true parameters." + "Let's start by looking at the normal distribution. In the code below we calculate the sample average and sample standard deviation and compare it to the true parameters. We sample from the normal distribution using numpy by calling `normal()`." ] }, { @@ -51,13 +51,18 @@ "def update_plots(expectation_mu, standard_dev, n, num_samples):\n", " estimates_expectation = []\n", " estimates_standard_deviation = []\n", + " errors_expectation = []\n", + " errors_standard_deviation = []\n", "\n", " # simulate samples\n", " for i in range(num_samples):\n", - " sample = np.random.normal(loc=expectation_mu, scale=standard_dev, size=n)\n", + " rng = np.random.default_rng()\n", + " sample = rng.normal(loc=expectation_mu, scale=standard_dev, size=n)\n", "\n", " expectation = sample.sum() / n\n", " standard_deviation = np.std(sample, ddof=1)\n", + " errors_expectation.append(expectation_mu - expectation)\n", + " errors_standard_deviation.append(standard_dev - standard_deviation)\n", " estimates_expectation.append(expectation)\n", " estimates_standard_deviation.append(standard_deviation)\n", "\n", @@ -66,8 +71,18 @@ " estimates_std = np.array(estimates_standard_deviation)\n", " mean_std = estimates_std.mean()\n", "\n", + " errors_exp = np.array(errors_expectation)\n", + " mse_exp = np.mean(errors_exp**2)\n", + "\n", + " errors_std = np.array(errors_standard_deviation)\n", + " mse_std = np.mean(errors_std**2)\n", + "\n", + " print(f\"MSE for mu = {mse_exp:.4f}\")\n", + " print(f\"MSE for sigma = {mse_std:.4f}\")\n", + "\n", " # plot estimates\n", " plt.figure(figsize=(15, 9))\n", + " plt.subplot(1, 2, 1)\n", " plt.hist(estimates_exp)\n", " plt.title(\"Estimation $\\hat{\\mu}$\")\n", " plt.ylabel(\"frequency\")\n", @@ -76,9 +91,11 @@ "\n", " plt.axvline(x=expectation_mu, color=\"r\", linestyle=\"--\")\n", " plt.axvline(x=mean_exp, color=\"y\", linestyle=\"--\")\n", - " plt.legend([\"true value\", \"mean of estimates\"])\n", + " plt.legend(\n", + " [f\"true value = {expectation_mu}\", f\"mean of estimates = {mean_exp:.2f}\"]\n", + " )\n", "\n", - " plt.figure(figsize=(15, 9))\n", + " plt.subplot(1, 2, 2)\n", " plt.hist(estimates_std)\n", " plt.title(\"Estimation $\\sigma$\")\n", " plt.ylabel(\"frequency\")\n", @@ -87,7 +104,7 @@ "\n", " plt.axvline(x=standard_dev, color=\"r\", linestyle=\"--\")\n", " plt.axvline(x=mean_std, color=\"y\", linestyle=\"--\")\n", - " plt.legend([\"true value\", \"mean of estimates\"])\n", + " plt.legend([f\"true value = {standard_dev}\", f\"mean of estimates = {mean_std:.2f}\"])\n", "\n", " plt.show()\n", "\n", @@ -108,6 +125,83 @@ ")\n", "interactive_plot" ] + }, + { + "cell_type": "markdown", + "id": "f5a00752", + "metadata": {}, + "source": [ + "Observe how the MSE and estimates are impacted by the sample size.\n", + "\n", + "Next, we will be looking at estimating the sample correlation given a multivariate normal distribution. Run the code below to calculate the sample correlation and compare it to the actual value.\n", + "\n", + "We sample from a multivariate normal distribution by passing the means and covariance of our distribution to numpy's `random.Generator.multivariate_normal` function. We calculate the Pearson's correlation coefficient using `np.corrcoef`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2895ae93", + "metadata": {}, + "outputs": [], + "source": [ + "def update_plots_multivariate(means, covariance, n, num_samples):\n", + " rng = np.random.default_rng()\n", + " estimates = []\n", + " errors = []\n", + " true_correlation = covariance[0][1] / (\n", + " np.sqrt(covariance[0][0]) * np.sqrt(covariance[1][1])\n", + " )\n", + "\n", + " # simulate samples\n", + " for i in range(num_samples):\n", + " sample = rng.multivariate_normal(means, covariance, size=n)\n", + " sample_correlation = np.corrcoef(sample, rowvar=False)[0, 1]\n", + " errors.append(true_correlation - sample_correlation)\n", + " estimates.append(sample_correlation)\n", + "\n", + " estimates = np.array(estimates)\n", + " mean_est = estimates.mean()\n", + "\n", + " errors = np.array(errors)\n", + " mse = np.mean(errors**2)\n", + "\n", + " print(f\"MSE for correlation = {mse:.4f}\")\n", + "\n", + " # plot estimates\n", + " plt.figure(figsize=(15, 9))\n", + " plt.hist(estimates)\n", + " plt.title(\"Estimation Correlation Coefficient\")\n", + " plt.ylabel(\"frequency\")\n", + " plt.xlabel(\"$estimates\")\n", + " plt.axvline(x=true_correlation, color=\"r\", linestyle=\"--\")\n", + " plt.axvline(x=mean_est, color=\"y\", linestyle=\"--\")\n", + " plt.legend(\n", + " [f\"true value = {true_correlation:.2f}\", f\"mean of estimates = {mean_est:.2f}\"]\n", + " )\n", + "\n", + " plt.show()\n", + "\n", + "\n", + "cov = 1.0\n", + "var_x = 0.5\n", + "var_y = 4.5\n", + "mu_true = 0.5\n", + "\n", + "samples = 100\n", + "slider_n = widgets.IntSlider(\n", + " value=100, min=100, max=1000, step=10, description=\"Sample Size\"\n", + ")\n", + "\n", + "interactive_plot = interactive(\n", + " update_plots_multivariate,\n", + " means=fixed([mu_true, mu_true]),\n", + " covariance=fixed([[var_x, cov], [cov, var_y]]),\n", + " n=slider_n,\n", + " num_samples=fixed(samples),\n", + ")\n", + "interactive_plot" + ] } ], "metadata": { diff --git a/book/estimation/ecdf_kde.ipynb b/book/estimation/ecdf_kde.ipynb index 81328ff..008aafa 100644 --- a/book/estimation/ecdf_kde.ipynb +++ b/book/estimation/ecdf_kde.ipynb @@ -7,9 +7,15 @@ "source": [ "# Histogram, Empirical Cumulative Distribution, Kernel Density Estimate\n", "\n", - "In this section we will be exploring these graphical estimates using simulation. We will start by sampling from the normal distribution and plotting a histogram, empirical cumulative distribution function (ECDF) and kernel density estimate (KDE).\n", + "In this section we will be exploring these graphical estimates using simulation. \n", "\n", - "We will start by comparing a histogram of the sample to the actual distribution. Adjust the sample size to see the effect on the histogram." + "## Continuous Data\n", + "\n", + "We will start by sampling from the normal distribution and plotting a histogram, empirical cumulative distribution function (ECDF) and kernel density estimate (KDE).\n", + "\n", + "We will start by comparing a histogram of the sample to the actual distribution. Adjust the sample size to see the effect on the histogram.\n", + "\n", + "We get the probability density function (PDF) of the normal distribution using scipy's `norm.pdf`." ] }, { @@ -40,7 +46,7 @@ "import matplotlib.pyplot as plt\n", "import ipywidgets as widgets\n", "from ipywidgets import interactive, fixed\n", - "from scipy.stats import norm, ecdf, expon\n", + "from scipy.stats import norm, ecdf, expon, binom\n", "import seaborn as sns" ] }, @@ -91,7 +97,7 @@ "id": "3c81d9bb", "metadata": {}, "source": [ - "As we can see, the histogram becomes a better estimator for the true distribution when the sample size is larger. A similar effect can be observed in the ECDF. Run the code below to compare the sample ECDF and theoretical ECDF." + "As we can see, the histogram becomes a better estimator for the true distribution when the sample size is larger. A similar effect can be observed in the ECDF. Run the code below to compare the sample ECDF and theoretical ECDF. We can calculate the ECDF of a sample using scipy's `ecdf` and `cdf.evaluate()` functions. To get the CDF of the normal distribution itself, we can call scipy's `norm.cdf` function." ] }, { @@ -142,7 +148,9 @@ "id": "baced830", "metadata": {}, "source": [ - "Finally, we analyze the KDE for the sample and compare it to the theoretical distribution. Again, feel free to adjust the sample size to see the effects." + "Finally, we analyze the KDE for the sample and compare it to the theoretical distribution. To do so, we will be using seaborn's `kdeplot` on the sampled data.\n", + "\n", + "Again, feel free to adjust the sample size to see the effects." ] }, { @@ -258,6 +266,144 @@ ")\n", "interactive_plot" ] + }, + { + "cell_type": "markdown", + "id": "011b82d9", + "metadata": {}, + "source": [ + "## Discrete Data\n", + "\n", + "In the previous section we looked at the normal and exponential distributions, these being continuous distributions. Now let us take a look at how these graphical estimators would look on a discrete distribution. The distribution we will be looking at is the binomial distribution.\n", + "\n", + "We will start by sampling from the binomial distribution and constructing a histogram based on this sample. Run the code below and adjust the sample size to observe the effect on the resulting histogram. We sample from the binomial distribution using numpy's `random.Generator.binomial` function. To get the probability mass function (PMF) of the distribution we use scipy's `binom.pmf` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a915e03b", + "metadata": {}, + "outputs": [], + "source": [ + "def update_binomial(n, p, sample_size):\n", + " r_vals = np.arange(n + 1)\n", + " dist_binomial = binom.pmf(r_vals, n, p)\n", + " rng = np.random.default_rng()\n", + " sample = rng.binomial(n, p, size=sample_size)\n", + " freqs = np.bincount(sample.astype(np.int32), minlength=n + 1) / sample_size\n", + "\n", + " plt.figure(figsize=(8, 5))\n", + " plt.bar(r_vals, freqs, alpha=0.5, label=\"Sampled Frequency\", color=\"orange\")\n", + " plt.plot(r_vals, dist_binomial, \"b-\", label=\"Theoretical PMF\", linewidth=2)\n", + " plt.xlabel(\"r\")\n", + " plt.ylabel(\"Probability\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + "\n", + "n = 10\n", + "p = 0.6\n", + "slider_n = widgets.IntSlider(value=5, min=5, max=100, step=5, description=\"Sample Size\")\n", + "interactive_plot = interactive(\n", + " update_binomial,\n", + " n=fixed(n),\n", + " p=fixed(p),\n", + " sample_size=slider_n,\n", + ")\n", + "interactive_plot" + ] + }, + { + "cell_type": "markdown", + "id": "b281564f", + "metadata": {}, + "source": [ + "Next we will look at the ECDF of the binomial distribution. Run the code below to plot the theoretical ECDF and the sample ECDF to compare the two. Adjust the sample size to observe the effect on the sample ECDF." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2fde4074", + "metadata": {}, + "outputs": [], + "source": [ + "def update_ecdf_binomial(n, p, sample_size):\n", + " x = np.arange(n + 1)\n", + "\n", + " data_norm = np.random.binomial(n, p, size=sample_size)\n", + " ecdf_data = ecdf(data_norm)\n", + " ecdf_y = ecdf_data.cdf.evaluate(x)\n", + "\n", + " actual_cdf = binom.cdf(x, n=n, p=p)\n", + "\n", + " plt.figure()\n", + " plt.plot(x, actual_cdf, \"b-\", linewidth=2, label=\"Theoretical Binomial CDF\")\n", + "\n", + " plt.plot(x, ecdf_y, \"r-\", label=\"Sample ECDF\")\n", + " plt.title(f\"ECDF of Binomial Distribution n = {n}, p = {p:.2f}\")\n", + " plt.xlabel(\"$X$\")\n", + " plt.ylabel(\"Frequency\")\n", + " plt.legend()\n", + "\n", + "\n", + "n = 10\n", + "p = 0.6\n", + "slider_n = widgets.IntSlider(value=5, min=5, max=100, step=5, description=\"Sample Size\")\n", + "interactive_plot = interactive(\n", + " update_ecdf_binomial,\n", + " n=fixed(n),\n", + " p=fixed(p),\n", + " sample_size=slider_n,\n", + ")\n", + "interactive_plot" + ] + }, + { + "cell_type": "markdown", + "id": "08bdc8c6", + "metadata": {}, + "source": [ + "Finally, we look at the KDE for a sample from the binomial distribution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0da49afd", + "metadata": {}, + "outputs": [], + "source": [ + "def update_binomial(n, p, sample_size):\n", + " r_vals = np.arange(n + 1)\n", + " dist_binomial = binom.pmf(r_vals, n, p)\n", + "\n", + " rng = np.random.default_rng()\n", + " sample = rng.binomial(n, p, size=sample_size)\n", + " freqs = np.bincount(sample.astype(np.int32), minlength=n + 1) / sample_size\n", + "\n", + " plt.figure(figsize=(8, 5))\n", + " plt.bar(r_vals, freqs, alpha=0.5, label=\"Sampled Frequency\", color=\"orange\")\n", + " plt.plot(r_vals, dist_binomial, \"b-\", label=\"Theoretical PMF\", linewidth=2)\n", + " sns.kdeplot(sample, fill=True, label=\"Sample KDE\", color=\"skyblue\", alpha=0.7)\n", + " plt.xlabel(\"r\")\n", + " plt.ylabel(\"Probability\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + "\n", + "n = 10\n", + "p = 0.6\n", + "slider_n = widgets.IntSlider(value=5, min=5, max=100, step=5, description=\"Sample Size\")\n", + "interactive_plot = interactive(\n", + " update_binomial,\n", + " n=fixed(n),\n", + " p=fixed(p),\n", + " sample_size=slider_n,\n", + ")\n", + "interactive_plot" + ] } ], "metadata": { diff --git a/book/estimation/misc/Law_of_Large_Nums.ipynb b/book/estimation/misc/Law_of_Large_Nums.ipynb index 28580fe..13eea69 100644 --- a/book/estimation/misc/Law_of_Large_Nums.ipynb +++ b/book/estimation/misc/Law_of_Large_Nums.ipynb @@ -40,8 +40,9 @@ "throws = 1000\n", "\n", "for i in range(throws):\n", - " d1 = np.random.uniform(1, 7, 1).astype(int)\n", - " d2 = np.random.uniform(1, 7, 1).astype(int)\n", + " rng = np.random.default_rng()\n", + " d1 = rng.uniform(1, 7, 1).astype(int)\n", + " d2 = rng.uniform(1, 7, 1).astype(int)\n", "\n", " if i == 0:\n", " running_avg_max.append(max(d1, d2))\n", diff --git a/book/estimation/misc/overview.md b/book/estimation/misc/overview.md new file mode 100644 index 0000000..941577a --- /dev/null +++ b/book/estimation/misc/overview.md @@ -0,0 +1,7 @@ +# Miscellaneous + + + + +## Chapter Overview + \ No newline at end of file From ed892a032da3c7dc31a20caee2569addb86090d3 Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Tue, 22 Jul 2025 09:22:10 -0400 Subject: [PATCH 19/22] remove misc material to be moved to a different section of book --- book/_toc.yml | 4 - .../misc/Central_Limit_Theorem.ipynb | 185 ------------------ book/estimation/misc/Law_of_Large_Nums.ipynb | 95 --------- book/estimation/misc/overview.md | 7 - 4 files changed, 291 deletions(-) delete mode 100644 book/estimation/misc/Central_Limit_Theorem.ipynb delete mode 100644 book/estimation/misc/Law_of_Large_Nums.ipynb delete mode 100644 book/estimation/misc/overview.md diff --git a/book/_toc.yml b/book/_toc.yml index 38ce9f9..69ec651 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -18,10 +18,6 @@ parts: - file: estimation/Confidence_Intervals.ipynb - file: estimation/avg_sd_correlation.ipynb - file: estimation/ecdf_kde.ipynb - - file: estimation/misc/overview.md - sections: - - file: estimation/misc/Central_Limit_Theorem.ipynb - - file: estimation/misc/Law_Of_Large_Nums.ipynb - caption: Miscellaneous chapters: - file: references.md diff --git a/book/estimation/misc/Central_Limit_Theorem.ipynb b/book/estimation/misc/Central_Limit_Theorem.ipynb deleted file mode 100644 index f14a63d..0000000 --- a/book/estimation/misc/Central_Limit_Theorem.ipynb +++ /dev/null @@ -1,185 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f4b600f7", - "metadata": {}, - "source": [ - "# Central Limit Theorem\n", - "\n", - "This section will be covering the central limit theorem (CLT) for sums and averages. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d0cd369", - "metadata": { - "tags": [ - "thebe-remove-input-init" - ] - }, - "outputs": [], - "source": [ - "import micropip\n", - "\n", - "await micropip.install(\"ipywidgets\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "44b6e199", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import scipy.stats as ss\n", - "import ipywidgets as widgets\n", - "from ipywidgets import interactive, fixed" - ] - }, - { - "cell_type": "markdown", - "id": "fd0b9d5c", - "metadata": {}, - "source": [ - "## Central Limit Theorem (for sums)\n", - "\n", - "The CLT for sums stipulates that for a sequence $X_i,...,X_n$ of independent and identically distributed random variables with expectation $E[X]=\\mu$ and variance $Var(X)=\\sigma ^ 2$ with large enough $n$, $S_n=\\sum^n_{i=1}X_i$ approximately has a normal distribution.\n", - "\n", - "$$\n", - "S_n \\sim N(n\\mu,n\\sigma ^ 2)\n", - "$$\n", - "\n", - "To illustrate the CLT in action, let us consider die throws. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b7fe659", - "metadata": {}, - "outputs": [], - "source": [ - "def dice_sum_simulation(n, n_simulations):\n", - " dice_rolls = np.random.randint(1, 7, size=(n_simulations, n))\n", - " sums = np.sum(dice_rolls, axis=1)\n", - " return sums\n", - "\n", - "\n", - "def update_plot(n_dice, n_sim):\n", - " mu_die = 3.5\n", - " var_die = 35 / 12\n", - "\n", - " mu_sum = n_dice * mu_die\n", - " var_sum = n_dice * var_die\n", - " sigma_sum = np.sqrt(var_sum)\n", - "\n", - " sums = dice_sum_simulation(n_dice, n_sim)\n", - "\n", - " plt.figure(figsize=(12, 8))\n", - " plt.hist(\n", - " sums,\n", - " bins=50,\n", - " density=True,\n", - " alpha=0.7,\n", - " color=\"skyblue\",\n", - " edgecolor=\"black\",\n", - " label=f\"Simulated Sums (n={n_sim})\",\n", - " )\n", - " x = np.linspace(sums.min(), sums.max(), 1000)\n", - " normal_pdf = ss.norm.pdf(x, loc=mu_sum, scale=sigma_sum)\n", - " plt.plot(\n", - " x,\n", - " normal_pdf,\n", - " \"r-\",\n", - " linewidth=3,\n", - " label=f\"Normal Approx: N({mu_sum:.1f}, {sigma_sum:.2f}²)\",\n", - " )\n", - "\n", - " plt.xlabel(\"Sum of Dice\")\n", - " plt.ylabel(\"Density/Probability\")\n", - " plt.title(f\"Distribution of Sum of {n_dice} Dice Throws\")\n", - " plt.legend()\n", - " plt.grid(True, alpha=0.3)\n", - "\n", - "\n", - "amt_simulations = 10000\n", - "\n", - "slider_n = widgets.IntSlider(value=2, min=2, max=150, step=2, description=\"Num dice\")\n", - "interactive_plot = interactive(\n", - " update_plot, n_dice=slider_n, n_sim=fixed(amt_simulations)\n", - ")\n", - "interactive_plot" - ] - }, - { - "cell_type": "markdown", - "id": "08486572", - "metadata": {}, - "source": [ - "## Central Limit Theorem (for averages)\n", - "\n", - "The CLT for averages stipulates that for a sequence $X_1,...,X_n$ of independent and identically distributed random variables with expectation $E[X]=\\mu$ and variance $Var(X)=\\sigma ^2$ with large enough $n$, $\\bar{X}_n = \\frac 1 n \\sum^n_{i=1} X_i$ approximately has a normal distribution.\n", - "\n", - "$$\n", - "\\bar{X}_n \\sim N(\\mu, \\frac{\\sigma ^ 2}{n})\n", - "$$\n", - "\n", - "To illustrate the CLT in action, let us consider an $Exp(1)$ distribution. We will plot the exact distribution and the CLT approximation with various $n$ values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3e710428", - "metadata": {}, - "outputs": [], - "source": [ - "def update_plots(lambda_true, n):\n", - " x = np.linspace(0, 4.5, 1000)\n", - "\n", - " pdf_exact = ss.gamma.pdf(x, a=n, scale=1 / (n * lambda_true))\n", - " pdf_normal = ss.norm.pdf(\n", - " x, loc=1 / lambda_true, scale=1 / (lambda_true * np.sqrt(n))\n", - " )\n", - "\n", - " plt.figure(figsize=(12, 9))\n", - "\n", - " plt.plot(\n", - " x,\n", - " pdf_normal,\n", - " label=f\"N({(1/lambda_true):.2f},{(1/(lambda_true * np.sqrt(n))):.2f})\",\n", - " color=\"tab:orange\",\n", - " )\n", - " plt.plot(\n", - " x, pdf_exact, label=f\"Mean of {n} i.i.d Exp({lambda_true})\", color=\"tab:blue\"\n", - " )\n", - " plt.legend()\n", - " plt.show()\n", - "\n", - "\n", - "lambda_val = 1.0\n", - "\n", - "slider_n = widgets.IntSlider(value=2, min=2, max=100, step=2, description=\"Size of n\")\n", - "interactive_plot = interactive(update_plots, n=slider_n, lambda_true=fixed(lambda_val))\n", - "interactive_plot" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "prob_stat_book", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.13.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/book/estimation/misc/Law_of_Large_Nums.ipynb b/book/estimation/misc/Law_of_Large_Nums.ipynb deleted file mode 100644 index 13eea69..0000000 --- a/book/estimation/misc/Law_of_Large_Nums.ipynb +++ /dev/null @@ -1,95 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8497a600", - "metadata": {}, - "source": [ - "# Law of Large Numbers\n", - "\n", - "This section we will be looking at the law of large numbers. This theorem states that for an independent and identically distributed sequence $X_1,...,X_n$ with $E[X_i]=\\mu$ and $E[X_i^2]<\\infty$ for any $\\epsilon > 0$ the following holds:\n", - "\n", - "$$\n", - "P(|\\bar{X}_n-\\mu|>\\epsilon) \\rightarrow 0, \\text{ as } n \\rightarrow \\infty\n", - "$$\n", - "\n", - "We can illustrate this theorem by considering a simulation of throwing two dice. We will simulate the mean of the maximum value of both dice and the mean of the sum of both dice over a large number of throws." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "60e026a2", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import scipy.stats as ss" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c27ea0e2", - "metadata": {}, - "outputs": [], - "source": [ - "running_avg_max = []\n", - "running_avg_sum = []\n", - "throws = 1000\n", - "\n", - "for i in range(throws):\n", - " rng = np.random.default_rng()\n", - " d1 = rng.uniform(1, 7, 1).astype(int)\n", - " d2 = rng.uniform(1, 7, 1).astype(int)\n", - "\n", - " if i == 0:\n", - " running_avg_max.append(max(d1, d2))\n", - " running_avg_sum.append(d1 + d2)\n", - " else:\n", - " running_avg_max.append((running_avg_max[-1] * i + max(d1, d2)) / (i + 1))\n", - " running_avg_sum.append((running_avg_sum[-1] * i + (d1 + d2)) / (i + 1))\n", - "\n", - "x = np.arange(1, throws + 1)\n", - "figure, axis = plt.subplots(2, 1, figsize=(14, 15))\n", - "axis[0].plot(x, running_avg_max, label=\"running average\")\n", - "axis[0].axhline(y=4.5, color=\"r\", linestyle=\"--\", label=\"true value\")\n", - "axis[0].set_title(\"Mean of max of two dice\")\n", - "axis[0].legend()\n", - "axis[0].set_xlabel(\"Throws\")\n", - "axis[0].set_ylabel(\"Mean number\")\n", - "\n", - "axis[1].plot(x, running_avg_sum, label=\"running average\")\n", - "axis[1].axhline(y=7, color=\"r\", linestyle=\"--\", label=\"true value\")\n", - "axis[1].set_title(\"Mean of sum of two dice\")\n", - "axis[1].legend()\n", - "axis[1].set_xlabel(\"Throws\")\n", - "axis[1].set_ylabel(\"Mean number\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "2f93ffc2", - "metadata": {}, - "source": [ - "As this simulation illustrates, with large $n$, the average of the trial results converges to the expected value." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "prob_stat_book", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.13.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/book/estimation/misc/overview.md b/book/estimation/misc/overview.md deleted file mode 100644 index 941577a..0000000 --- a/book/estimation/misc/overview.md +++ /dev/null @@ -1,7 +0,0 @@ -# Miscellaneous - - - - -## Chapter Overview - \ No newline at end of file From a6c47fcf463704b5b6262786e4e1643f5dd2a2c0 Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Tue, 29 Jul 2025 11:59:10 -0400 Subject: [PATCH 20/22] add fixes to 911 calls, birth months, and MLE --- book/estimation/911_Calls_Estimation.ipynb | 66 ++++++++- book/estimation/Birth_Month_Estimation.ipynb | 61 ++++----- .../Maximum_Likelihood_Estimate.ipynb | 125 +++--------------- 3 files changed, 103 insertions(+), 149 deletions(-) diff --git a/book/estimation/911_Calls_Estimation.ipynb b/book/estimation/911_Calls_Estimation.ipynb index 70ca718..367e5ce 100644 --- a/book/estimation/911_Calls_Estimation.ipynb +++ b/book/estimation/911_Calls_Estimation.ipynb @@ -7,15 +7,33 @@ "source": [ "# 911 Calls\n", "\n", - "In this section we will be using data on 911 calls in Fernhaven to estimate the probability of having no 911 calls in a minute. This distribution is modeled using a Poisson distribution with probability mass function \n", + "In this section we will be using data on 911 calls in Fernhaven to estimate the probability of having no 911 calls in a minute. \n", + "\n", + "\n", "\n", + "We will be covering two methods of estimation: proportion and method of moments/maximum likelihood estimate (MLE)." + ] + }, + { + "cell_type": "markdown", + "id": "35f13c30", + "metadata": {}, + "source": [ + "```{note}\n", + "In this case the method of moments and maximum likelihood estimator coincide. However, this is **not** always the case.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "1901c494", + "metadata": {}, + "source": [ "The first step is to perform *data wrangling* on the dataset to get several datasets of the number of calls per minute with $n=60$ (i.e. an hour long). In order to assume the same distribution across these datasets we will use the same time of day for each dataset." ] }, @@ -41,13 +59,18 @@ "# Read in the data and extract calls between 6 and 7\n", "df = pd.read_csv(\"data/911_Calls.csv\")\n", "\n", + "# Convert 'Date' and 'Call time' to a datetime object\n", "df[\"datetime\"] = pd.to_datetime(df[\"Date\"] + \" \" + df[\"Call time\"])\n", "\n", + "# Extract date, hour, and minute from the datetime object\n", "df[\"date\"] = df[\"datetime\"].dt.date.astype(str)\n", "df[\"hour\"] = df[\"datetime\"].dt.hour\n", "df[\"minute\"] = df[\"datetime\"].dt.minute\n", "\n", + "# Filter calls that are between 6:00 and 6:59\n", "df_hour = df[df[\"hour\"] == 6]\n", + "\n", + "# Group by date and minute, counting the number of calls\n", "counts = df_hour.groupby([\"date\", \"minute\"]).size().reset_index(name=\"count\")\n", "\n", "hourly_counts = {}\n", @@ -122,7 +145,17 @@ "id": "946682b2", "metadata": {}, "source": [ - "We can now make histograms of the obtained estimates for both cases to estimate the distribution and indicate the deviation from the true parameter. The true parameter in this case will be obtained using the full data-set." + "We can now make histograms of the obtained estimates for both cases to estimate the distribution and indicate the deviation from the true parameter. The true parameter in this case will be obtained using the full data-set. " + ] + }, + { + "cell_type": "markdown", + "id": "91407ce1", + "metadata": {}, + "source": [ + "```{note}\n", + "Although not exact, using the full dataset will be a very very good estimate of the true parameter. For this reason we will use it as the true value.\n", + "```" ] }, { @@ -170,7 +203,11 @@ "source": [ "Given these two estimators, we need to determine the best one. $\\hat{p}$ is an unbiased estimator, but $e^{-\\hat{\\mu}}$ is positively biased. From the histograms, we also observe that $\\hat{p}$ has a larger variance than $e^{-\\hat{\\mu}}$. This translates to being typically far away from the true value versus being typically close to a value above the true value. We typically want to select the estimator with the lowest mean squared error (MSE).\n", "\n", - "Using the true value from the dataset we can estimate the MSE value for both estimators. Our true value will be the calculated by considering all phone calls taken between 6 and 7." + "Using the true value from the dataset we can estimate the MSE value for both estimators. Our true value will be the calculated by considering all phone calls taken between 6 and 7. Using the MSE and bias we can calculate the variance of both estimators due to the fact that\n", + "\n", + "$$\n", + "\\text{MSE} - \\text{bias}^2 = \\text{variance}\n", + "$$" ] }, { @@ -180,6 +217,7 @@ "metadata": {}, "outputs": [], "source": [ + "# calculate the true parameter over the full dataset\n", "df_full = pd.read_csv(\"data/911_Calls.csv\")\n", "df_full[\"datetime\"] = pd.to_datetime(df_full[\"Date\"] + \" \" + df_full[\"Call time\"])\n", "df_full[\"date\"] = df_full[\"datetime\"].dt.date.astype(str)\n", @@ -194,10 +232,16 @@ " full = series.reindex(range(60), fill_value=0)\n", " hourly_counts[date] = full.tolist()\n", "\n", + "# calculate the number of times there were 0 calls in a minute\n", "total_zero_minutes = np.sum([counts.count(0) for counts in hourly_counts.values()])\n", + "\n", + "# calculate the total number of minutes\n", "total_minutes = len(hourly_counts) * 60\n", + "\n", + "# calculate the actual proportion of zero calls\n", "actual_prop = total_zero_minutes / total_minutes\n", "\n", + "# calculate MSE and bias for both estimators\n", "mse_prop = np.sum((actual_prop - p_hat) ** 2) / len(p_hat)\n", "bias_prop = np.mean(p_hat) - actual_prop\n", "\n", @@ -214,6 +258,16 @@ "print(f\"MSE - Bias^2 for proportion: {mse_prop - bias_prop ** 2}\")\n", "print(f\"MSE - Bias^2 for MLE: {mse_mle - bias_mle ** 2}\")" ] + }, + { + "cell_type": "markdown", + "id": "66508de3", + "metadata": {}, + "source": [ + "```{note}\n", + "You may notice that the bias calculated for proportion is 0. This holds because the estimator is unbiased!\n", + "```" + ] } ], "metadata": { diff --git a/book/estimation/Birth_Month_Estimation.ipynb b/book/estimation/Birth_Month_Estimation.ipynb index 81bb6a0..47aa361 100644 --- a/book/estimation/Birth_Month_Estimation.ipynb +++ b/book/estimation/Birth_Month_Estimation.ipynb @@ -109,19 +109,18 @@ "\n", "plt.subplot(1, 3, 2)\n", "sns.countplot(x=\"Month\", data=df_sample2, order=months, stat=\"proportion\")\n", - "plt.title(\"Distribution of sample 1\")\n", + "plt.title(\"Distribution of sample 2\")\n", "plt.xlabel(\"Month\")\n", "plt.ylabel(\"Proportion\")\n", "plt.xticks(rotation=45)\n", "\n", "plt.subplot(1, 3, 3)\n", "sns.countplot(x=\"Month\", data=df_sample3, order=months, stat=\"proportion\")\n", - "plt.title(\"Distribution of sample 1\")\n", + "plt.title(\"Distribution of sample 3\")\n", "plt.xlabel(\"Month\")\n", "plt.ylabel(\"Proportion\")\n", "plt.xticks(rotation=45)\n", "\n", - "\n", "plt.tight_layout()\n", "plt.show()" ] @@ -143,60 +142,48 @@ "metadata": {}, "outputs": [], "source": [ + "def get_month_proportions(data, months):\n", + " counts = pd.Series(data).value_counts()\n", + " proportions = []\n", + " for month in months:\n", + " proportions.append(counts.get(month, 0) / len(data))\n", + " return proportions\n", + "\n", "def update_plot(n):\n", + " \n", + " w, x = 0.25, np.arange(len(months))\n", "\n", " # naive model for months\n", " naive_months = np.random.choice(months, size=n, p=[1 / 12 for i in range(12)])\n", - " df_naive_months = pd.DataFrame(naive_months, columns=[\"Month\"])\n", "\n", " # estimate using entire dataset\n", - " entire_dataset = pd.DataFrame({\"Month\": df[\"birth_month\"].values})\n", + " entire_dataset = df[\"birth_month\"].values\n", "\n", " # sample from dataset\n", " sample_dataset = df[\"birth_month\"].sample(n=n)\n", - " df_sample_dataset = pd.DataFrame({\"Month\": sample_dataset.values})\n", + " \n", + " naive_props = get_month_proportions(naive_months, months)\n", + " entire_props = get_month_proportions(entire_dataset, months)\n", + " sample_props = get_month_proportions(sample_dataset, months)\n", "\n", "\n", - " plt.figure(figsize=(15, 5))\n", + " fig, ax = plt.subplots(figsize=(14, 6))\n", + " plt.bar(x - w, naive_props, w, label='Naive Model')\n", + " plt.bar(x, entire_props, w, label='Entire Dataset')\n", + " plt.bar(x + w, sample_props, w, label='Sample')\n", "\n", - " plt.subplot(1, 3, 1)\n", - " sns.countplot(x=\"Month\", data=df_naive_months, order=months, stat=\"proportion\")\n", " plt.title(f\"Distribution: Naive Uniform on Months with n={n}\")\n", " plt.xlabel(\"Month\")\n", " plt.ylabel(\"Proportion\")\n", + " ax.set_xticks(x)\n", + " ax.set_xticklabels(months, rotation=45)\n", " plt.xticks(rotation=45)\n", - "\n", - "\n", - " plt.subplot(1, 3, 2)\n", - " sns.countplot(\n", - " x=\"Month\",\n", - " data=entire_dataset,\n", - " order=months,\n", - " stat=\"proportion\",\n", - " )\n", - " plt.title(\"Distribution: Entire Dataset\")\n", - " plt.xlabel(\"Month\")\n", - " plt.ylabel(\"Proportion\")\n", - " plt.xticks(rotation=45)\n", - "\n", - "\n", - " plt.subplot(1, 3, 3)\n", - " sns.countplot(\n", - " x=\"Month\",\n", - " data=df_sample_dataset,\n", - " order=months,\n", - " stat=\"proportion\",\n", - " )\n", - " plt.title(f\"Distribution: Sample of Dataset with n={n}\")\n", - " plt.xlabel(\"Month\")\n", - " plt.ylabel(\"Proportion\")\n", - " plt.xticks(rotation=45)\n", - "\n", + " ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)\n", " plt.tight_layout()\n", " plt.show()\n", "\n", "slider_n = widgets.IntSlider(\n", - " value=255, min=10, max=500, step=10, description=\"Sample Size\"\n", + " value=5000, min=10, max=10000, step=10, description=\"Sample Size\"\n", ")\n", "interactive_plot = interactive(\n", " update_plot,\n", diff --git a/book/estimation/Maximum_Likelihood_Estimate.ipynb b/book/estimation/Maximum_Likelihood_Estimate.ipynb index 64c6a3a..559ef5c 100644 --- a/book/estimation/Maximum_Likelihood_Estimate.ipynb +++ b/book/estimation/Maximum_Likelihood_Estimate.ipynb @@ -15,51 +15,11 @@ "id": "8e8754b1", "metadata": {}, "source": [ - "We will start by deriving the MLE for the exponential distribution. The parameter we want to estimate is $\\lambda$. We start with the probability density function for the exponential distribution: \n", + "We will start by deriving the MLE for the exponential distribution. The parameter we want to estimate is $\\lambda$. Using standard calculus techniques we find that:\n", "\n", "$$\n", - "f(x)=\\lambda e^{-\\lambda x}\n", - "$$\n", - "\n", - "Now we can use this to calculate the likelihood and log likelihood.\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - "L(\\lambda) &= \\prod_{i=1}^n f(x_i) \\\\\n", - "&= \\prod_{i=1}^n \\lambda e^{-\\lambda x_i}\\\\\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - "\\ln L(\\lambda) &= \\ln \\left[ \\prod_{i=1}^n \\lambda e^{-\\lambda x_i} \\right] \\\\\n", - "&= \\sum_{i=1}^n \\ln \\left[ \\lambda e^{-\\lambda x_i} \\right] \\\\\n", - "&= n \\ln \\lambda - \\lambda \\sum_{i=1}^n x_i \\\\\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "\n", - "\n", - "Using the likelihood function above, we arrive at $\\hat{\\lambda} = \\frac{n}{\\sum_{i=1}^n x_i}$." + "\\hat{\\lambda} = \\frac{1}{\\bar{x}}\n", + "$$" ] }, { @@ -67,63 +27,15 @@ "id": "61b6df4b", "metadata": {}, "source": [ - "Next we will derive the MLE for the normal distribution parameters $\\mu$ and $\\sigma$. We start again with the probability density function for the normal distribution.\n", + "Next we derive the MLE for the normal distribution parameters $\\mu$ and $\\sigma$. We arrive at:\n", "\n", "$$\n", - "f(x)=\\frac{1}{\\sigma \\sqrt{2 \\pi}}e^{-\\frac 1 2 (\\frac{x-\\mu}{\\sigma})^2}\n", + "\\hat{\\mu} = \\text{average} = \\bar{x}\n", "$$\n", "\n", - "Now we calculate the likelihood and log-likelihood.\n", - "\n", "$$\n", - "\\begin{aligned}\n", - "L &= \\prod_{i=1}^n f(x_i) \\\\\n", - "&= \\prod_{i=1}^n \\frac{1}{\\sigma \\sqrt{2 \\pi}}e^{-\\frac 1 2 (\\frac{x_i-\\mu}{\\sigma})^2} \\\\\n", - "\\ln L &= \\ln \\left[ \\prod_{i=1}^n \\frac{1}{\\sigma \\sqrt{2 \\pi}}e^{-\\frac 1 2 (\\frac{x_i-\\mu}{\\sigma})^2} \\right] \\\\\n", - "&= \\sum_{i=1}^n \\ln \\left[ \\frac{1}{\\sigma \\sqrt{2 \\pi}}e^{-\\frac 1 2 (\\frac{x_i-\\mu}{\\sigma})^2} \\right] \\\\\n", - "&= -\\frac n 2 \\ln (2\\pi) - \\frac n 2 \\ln (\\sigma ^ 2) - \\frac 1 {2\\sigma ^2} \\sum_{i=1}^n (x_i - \\mu)^2\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "We arrive at $\\hat{\\mu} = \\frac 1 n \\sum_{i=1}^n x_i$ and $\\hat{\\sigma} = \\sqrt{\\frac{\\sum_{i=1}^n (x_i - \\mu)^2}{n}}$." + "\\hat{\\sigma} = \\sqrt{\\frac{\\sum_{i=1}^n (x_i - \\mu)^2}{n}}\n", + "$$" ] }, { @@ -131,7 +43,7 @@ "id": "8f8bd062", "metadata": {}, "source": [ - "Now that we have calculated these estimators, let's confirm that they accurately estimate where the likelihood is maximized. To do so, we can graph the likelihood and log-likelihood functions of each and the corresponding MLE." + "Now that we have calculated these estimators, let's confirm that they accurately estimate the true parameter and show where the likelihood is maximized. To do so, we can graph the likelihood and log-likelihood functions of each and the corresponding MLE." ] }, { @@ -171,23 +83,25 @@ "outputs": [], "source": [ "def update_plot(sample_size, lambda_val):\n", + " # we use the Generator API as it is a modern version of RandomState, constructing a new generator is done in the following line\n", " rng = np.random.default_rng()\n", + "\n", + " # we use the generator to create an exponential distribution sample\n", " data_exp = rng.exponential(scale=1 / lambda_val, size=sample_size)\n", "\n", " lambdas = np.linspace(0.01, 1.5, 500)\n", - " n = data_exp.size\n", " sum_x = data_exp.sum()\n", "\n", " # likelihood for lambda\n", - " likelihood = (lambdas**n) * np.exp(-lambdas * sum_x)\n", + " likelihood = (lambdas**sample_size) * np.exp(-lambdas * sum_x)\n", "\n", " # log likelihood for lambda\n", - " logL = n * np.log(lambdas) - lambdas * sum_x\n", + " logL = np.log(likelihood)\n", "\n", " plt.figure(figsize=(15, 6))\n", " plt.subplot(1, 2, 1)\n", " plt.plot(lambdas, likelihood)\n", - " plt.axvline(n / sum_x, color=\"C1\", ls=\"--\", label=f\"MLE $\\lambda$={n/sum_x:.2f}\")\n", + " plt.axvline(sample_size / sum_x, color=\"C1\", ls=\"--\", label=f\"MLE $\\lambda$={sample_size/sum_x:.2f}\")\n", " plt.axvline(\n", " lambda_val,\n", " color=\"tab:olive\",\n", @@ -201,7 +115,7 @@ "\n", " plt.subplot(1, 2, 2)\n", " plt.plot(lambdas, logL)\n", - " plt.axvline(n / sum_x, color=\"C1\", ls=\"--\", label=f\"MLE $\\lambda$={n/sum_x:.2f}\")\n", + " plt.axvline(sample_size / sum_x, color=\"C1\", ls=\"--\", label=f\"MLE $\\lambda$={sample_size/sum_x:.2f}\")\n", " plt.axvline(\n", " lambda_val,\n", " color=\"tab:olive\",\n", @@ -236,7 +150,6 @@ " rng = np.random.default_rng()\n", " data_norm = rng.normal(loc=mu_val, scale=2.0, size=n)\n", " mu = np.linspace(-2, 5, 200)\n", - " n = data_norm.size\n", " sigma0 = data_norm.std(ddof=1)\n", "\n", " likelihood = []\n", @@ -426,12 +339,12 @@ " plt.figure(figsize=(15, 6))\n", " plt.subplot(1, 2, 1)\n", " plt.plot(lambdas, likelihood)\n", - " plt.axvline(sum_x / n, color=\"C1\", ls=\"--\", label=f\"MLE $\\lambda$={sum_x/n:.2f}\")\n", + " plt.axvline(sum_x / n, color=\"C1\", ls=\"--\", label=f\"MLE $E[X]$={sum_x/n:.2f}\")\n", " plt.axvline(\n", " 1.0 / lambda_val,\n", " color=\"tab:olive\",\n", " ls=\"--\",\n", - " label=f\"True value $\\lambda$={1.0/lambda_val:.2f}\",\n", + " label=f\"True value $E[X]$={1.0/lambda_val:.2f}\",\n", " )\n", " plt.title(\"Exponential Likelihood\")\n", " plt.xlabel(\"$E[X]$\")\n", @@ -440,12 +353,12 @@ "\n", " plt.subplot(1, 2, 2)\n", " plt.plot(lambdas, logL)\n", - " plt.axvline(sum_x / n, color=\"C1\", ls=\"--\", label=f\"MLE $\\lambda$={sum_x/n:.2f}\")\n", + " plt.axvline(sum_x / n, color=\"C1\", ls=\"--\", label=f\"MLE $E[X]$={sum_x/n:.2f}\")\n", " plt.axvline(\n", " 1.0 / lambda_val,\n", " color=\"tab:olive\",\n", " ls=\"--\",\n", - " label=f\"True value $\\lambda$={1.0/lambda_val:.2f}\",\n", + " label=f\"True value $E[X]={1.0/lambda_val:.2f}\",\n", " )\n", " plt.title(\"Exponential Log-Likelihood\")\n", " plt.xlabel(\"$E[X]$\")\n", From 5df1b96823e255cf47d2c47227477b188d36b4fe Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Wed, 30 Jul 2025 10:53:19 -0400 Subject: [PATCH 21/22] add theoretical mse and mse for variance, update plots for MLE and add additional notes --- .../Maximum_Likelihood_Estimate.ipynb | 39 +++- book/estimation/Mean_Squared_Error.ipynb | 166 ++++++++++++++---- 2 files changed, 163 insertions(+), 42 deletions(-) diff --git a/book/estimation/Maximum_Likelihood_Estimate.ipynb b/book/estimation/Maximum_Likelihood_Estimate.ipynb index 559ef5c..e3a8f55 100644 --- a/book/estimation/Maximum_Likelihood_Estimate.ipynb +++ b/book/estimation/Maximum_Likelihood_Estimate.ipynb @@ -101,7 +101,12 @@ " plt.figure(figsize=(15, 6))\n", " plt.subplot(1, 2, 1)\n", " plt.plot(lambdas, likelihood)\n", - " plt.axvline(sample_size / sum_x, color=\"C1\", ls=\"--\", label=f\"MLE $\\lambda$={sample_size/sum_x:.2f}\")\n", + " plt.axvline(\n", + " sample_size / sum_x,\n", + " color=\"C1\",\n", + " ls=\"--\",\n", + " label=f\"MLE $\\lambda$={sample_size/sum_x:.2f}\",\n", + " )\n", " plt.axvline(\n", " lambda_val,\n", " color=\"tab:olive\",\n", @@ -115,7 +120,12 @@ "\n", " plt.subplot(1, 2, 2)\n", " plt.plot(lambdas, logL)\n", - " plt.axvline(sample_size / sum_x, color=\"C1\", ls=\"--\", label=f\"MLE $\\lambda$={sample_size/sum_x:.2f}\")\n", + " plt.axvline(\n", + " sample_size / sum_x,\n", + " color=\"C1\",\n", + " ls=\"--\",\n", + " label=f\"MLE $\\lambda$={sample_size/sum_x:.2f}\",\n", + " )\n", " plt.axvline(\n", " lambda_val,\n", " color=\"tab:olive\",\n", @@ -150,12 +160,14 @@ " rng = np.random.default_rng()\n", " data_norm = rng.normal(loc=mu_val, scale=2.0, size=n)\n", " mu = np.linspace(-2, 5, 200)\n", - " sigma0 = data_norm.std(ddof=1)\n", "\n", " likelihood = []\n", " logL_mu = []\n", "\n", " for m in mu:\n", + " # mle of sigma\n", + " sigma0 = np.sqrt(np.mean((data_norm - m) ** 2))\n", + "\n", " log_likelihood = -n * np.log(sigma0 * np.sqrt(2 * np.pi)) - np.sum(\n", " (data_norm - m) ** 2\n", " ) / (2 * sigma0**2)\n", @@ -197,7 +209,7 @@ "\n", "mu_true = 1.0\n", "slider_n = widgets.IntSlider(\n", - " value=50, min=10, max=200, step=10, description=\"Sample Size\"\n", + " value=50, min=10, max=250, step=10, description=\"Sample Size\"\n", ")\n", "interactive_plot = interactive(update_plot_mu, n=slider_n, mu_val=fixed(mu_true))\n", "interactive_plot" @@ -215,6 +227,7 @@ " data_norm = rng.normal(loc=1.0, scale=sigma_val, size=n)\n", " sigma = np.linspace(0.1, 5, 200)\n", "\n", + " # mle of mu\n", " mu0 = data_norm.mean()\n", " likelihood = []\n", " logL_sigma = []\n", @@ -284,6 +297,24 @@ "interactive_plot" ] }, + { + "cell_type": "markdown", + "id": "83c8e48e", + "metadata": {}, + "source": [ + "Run the code block below to visualise the surface with the likelihood as a function of both $\\mu$ and $\\sigma$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4be6ce82", + "metadata": {}, + "outputs": [], + "source": [ + "# add code for 3d plot" + ] + }, { "cell_type": "markdown", "id": "7687f366", diff --git a/book/estimation/Mean_Squared_Error.ipynb b/book/estimation/Mean_Squared_Error.ipynb index f8561eb..7b44805 100644 --- a/book/estimation/Mean_Squared_Error.ipynb +++ b/book/estimation/Mean_Squared_Error.ipynb @@ -101,7 +101,7 @@ "id": "d8985b2c", "metadata": {}, "source": [ - "Now lets investigate the impact of $\\lambda$ on the MSE. In the code block below we plot the calculated MSE versus the value of $\\lambda$. Observe the relationship between $\\lambda$ and the MSE." + "Now lets investigate the impact of $\\lambda$ on the MSE. In the code block below we plot the calculated MSE versus the value of $\\lambda$ empirically and theoretically. Observe the relationship between $\\lambda$ and the MSE." ] }, { @@ -113,6 +113,7 @@ "source": [ "def simulate_exponential(lambda_vals, sample_size=100, samples=1000):\n", " mse = []\n", + " mse_theoretical = []\n", " # simulate samples\n", " for i in lambda_vals:\n", " errors = []\n", @@ -126,25 +127,35 @@ "\n", " errors = np.array(errors)\n", " mse.append(np.mean(errors**2))\n", + " mse_theoretical.append(\n", + " i**2 * (sample_size + 2) / ((sample_size - 2) * (sample_size - 1))\n", + " )\n", "\n", - " return mse\n", + " return mse, mse_theoretical\n", "\n", "\n", - "lambda_vals = np.linspace(0.1, 2, 150)\n", - "sample_size = 100\n", - "samples = 1000\n", - "mse_vals = simulate_exponential(lambda_vals, sample_size, samples)\n", + "def update_plot(n):\n", + " lambda_vals = np.linspace(0.1, 2, 150)\n", + " samples = 100\n", + " mse_vals, mse_theoretical_vals = simulate_exponential(lambda_vals, n, samples)\n", "\n", - "plt.figure(figsize=(14, 8))\n", + " plt.figure(figsize=(14, 8))\n", "\n", + " plt.plot(lambda_vals, mse_vals, label=\"empirical values\")\n", + " plt.plot(lambda_vals, mse_theoretical_vals, label=\"theoretical values\")\n", + " plt.title(\"Empirical Mean Squared Error vs Lambda Values\")\n", + " plt.legend()\n", + " plt.xlabel(\"Lambda Values\")\n", + " plt.ylabel(\"MSE\")\n", "\n", - "plt.plot(lambda_vals, mse_vals, label=\"mse values\")\n", - "plt.title(\"Mean Squared Error vs Lambda Values\")\n", - "plt.legend()\n", - "plt.xlabel(\"Lambda Values\")\n", - "plt.ylabel(\"MSE\")\n", + " plt.show()\n", "\n", - "plt.show()" + "\n", + "slider_n = widgets.IntSlider(value=50, min=10, max=500, step=10, description=\"n\")\n", + "\n", + "\n", + "interactive_plot = interactive(update_plot, n=slider_n)\n", + "interactive_plot" ] }, { @@ -217,7 +228,7 @@ "id": "ac4b7d74", "metadata": {}, "source": [ - "Again, we investigate the impact of the value of $\\mu$ on the MSE. Run the code below to plot the MSE versus the value of $\\mu$ and observe the relationship between the two values." + "We will explore the dependence of the MSE on $\\mu$ as well as $\\sigma$. Run the code blocks below to calculate the MSE for values of $\\mu$ and $\\sigma$ and observe the relationship." ] }, { @@ -229,6 +240,7 @@ "source": [ "def simulate_normal(mu_vals, sample_size=100, samples=1000):\n", " mse = []\n", + " mse_theoretical = []\n", " # simulate samples\n", " for i in mu_vals:\n", " errors = []\n", @@ -242,25 +254,95 @@ "\n", " errors = np.array(errors)\n", " mse.append(np.mean(errors**2))\n", + " mse_theoretical.append(1.0 / sample_size)\n", "\n", - " return mse\n", + " return mse, mse_theoretical\n", "\n", "\n", - "mu_vals = np.linspace(0, 2, 150)\n", - "sample_size = 100\n", - "samples = 1000\n", - "mse_vals = simulate_normal(mu_vals, sample_size, samples)\n", + "def update_plot(n):\n", "\n", - "plt.figure(figsize=(14, 8))\n", + " mu_vals = np.linspace(0, 2, 150)\n", + " samples = 1000\n", + " mse_vals, mse_theoretical_vals = simulate_normal(mu_vals, n, samples)\n", "\n", + " plt.figure(figsize=(14, 8))\n", "\n", - "plt.plot(lambda_vals, mse_vals, label=\"mse values\")\n", - "plt.title(\"Mean Squared Error vs Mu Values\")\n", - "plt.legend()\n", - "plt.xlabel(\"Mu Values\")\n", - "plt.ylabel(\"MSE\")\n", + " plt.plot(mu_vals, mse_vals, label=\"empirical values\")\n", + " plt.plot(mu_vals, mse_theoretical_vals, label=\"theoretical values\")\n", + " plt.title(\"Mean Squared Error vs Mu Values\")\n", + " plt.legend()\n", + " plt.xlabel(\"Mu Values\")\n", + " plt.ylabel(\"MSE\")\n", "\n", - "plt.show()" + " plt.show()\n", + "\n", + "\n", + "slider_n = widgets.IntSlider(value=50, min=10, max=500, step=10, description=\"n\")\n", + "\n", + "interactive_plot = interactive(update_plot, n=slider_n)\n", + "interactive_plot" + ] + }, + { + "cell_type": "markdown", + "id": "1f39d4c1", + "metadata": {}, + "source": [ + "```{note}\n", + "Although the plot seems very choppy, it is important to pay attention to the scale of the y-axis when performing analysis. In reality the empirical MSE is very close to the theoretical value.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1a7f846", + "metadata": {}, + "outputs": [], + "source": [ + "def simulate_normal(variance_vals, sample_size=100, samples=1000):\n", + " mse = []\n", + " mse_theoretical = []\n", + " # simulate samples\n", + " for i in variance_vals:\n", + " errors = []\n", + " for j in range(samples):\n", + " rng = np.random.default_rng()\n", + " sample = rng.normal(loc=2.0, scale=np.sqrt(i), size=sample_size)\n", + "\n", + " mle = np.mean((sample - 2.0) ** 2)\n", + "\n", + " error = i - mle\n", + " errors.append(error)\n", + "\n", + " errors = np.array(errors)\n", + " mse.append(np.mean(errors**2))\n", + " mse_theoretical.append(i / (sample_size))\n", + "\n", + " return mse, mse_theoretical\n", + "\n", + "\n", + "def update_plot(n):\n", + " sigma_vals = np.linspace(0, 9, 150)\n", + " samples = 100\n", + " mse_vals, mse_theoretical_vals = simulate_normal(sigma_vals, n, samples)\n", + "\n", + " plt.figure(figsize=(14, 8))\n", + "\n", + " plt.plot(sigma_vals, mse_vals, label=\"empirical values\")\n", + " plt.plot(sigma_vals, mse_theoretical_vals, label=\"theoretical values\")\n", + " plt.title(\"Mean Squared Error vs Variance\")\n", + " plt.legend()\n", + " plt.xlabel(\"Variance\")\n", + " plt.ylabel(\"MSE\")\n", + "\n", + " plt.show()\n", + "\n", + "\n", + "slider_n = widgets.IntSlider(value=50, min=10, max=500, step=10, description=\"n\")\n", + "\n", + "interactive_plot = interactive(update_plot, n=slider_n)\n", + "interactive_plot" ] }, { @@ -349,6 +431,7 @@ "source": [ "def simulate_exponential_bad(lambda_vals, sample_size=100, samples=1000):\n", " mse = []\n", + " mse_theoretical = []\n", " # simulate samples\n", " for i in lambda_vals:\n", " errors = []\n", @@ -362,25 +445,32 @@ "\n", " errors = np.array(errors)\n", " mse.append(np.mean(errors**2))\n", + " mse_theoretical.append(1 / (i**2))\n", "\n", - " return mse\n", + " return mse, mse_theoretical\n", "\n", "\n", - "lambda_vals = np.linspace(0.1, 2, 150)\n", - "sample_size = 100\n", - "samples = 1000\n", - "mse_vals = simulate_exponential_bad(lambda_vals, sample_size, samples)\n", + "def update_plot(n):\n", + " lambda_vals = np.linspace(0.1, 2, 150)\n", + " samples = 100\n", + " mse_vals, mse_theoretical_vals = simulate_exponential_bad(lambda_vals, n, samples)\n", "\n", - "plt.figure(figsize=(14, 8))\n", + " plt.figure(figsize=(14, 8))\n", "\n", + " plt.plot(lambda_vals, mse_vals, label=\"empirical values\")\n", + " plt.plot(lambda_vals, mse_theoretical_vals, label=\"theoretical values\")\n", + " plt.title(\"Mean Squared Error vs Lambda Values (estimating $1/\\lambda$)\")\n", + " plt.legend()\n", + " plt.xlabel(\"Lambda Values\")\n", + " plt.ylabel(\"MSE\")\n", "\n", - "plt.plot(lambda_vals, mse_vals, label=\"mse values\")\n", - "plt.title(\"Mean Squared Error vs Lambda Values (estimating $1/\\lambda$)\")\n", - "plt.legend()\n", - "plt.xlabel(\"Lambda Values\")\n", - "plt.ylabel(\"MSE\")\n", + " plt.show()\n", "\n", - "plt.show()" + "\n", + "slider_n = widgets.IntSlider(value=50, min=10, max=500, step=10, description=\"n\")\n", + "\n", + "interactive_plot = interactive(update_plot, n=slider_n)\n", + "interactive_plot" ] }, { From ddea1a2603c782eb34f220825ac64a6e377eee09 Mon Sep 17 00:00:00 2001 From: Stella Schultz Date: Mon, 4 Aug 2025 09:41:11 -0400 Subject: [PATCH 22/22] add 3D plot for MLE, add code comments and reformat code, update plot size and clarity in ecdf, histogram, kde section --- .../Maximum_Likelihood_Estimate.ipynb | 65 ++++++++++++++++++- book/estimation/ecdf_kde.ipynb | 31 ++++++--- 2 files changed, 85 insertions(+), 11 deletions(-) diff --git a/book/estimation/Maximum_Likelihood_Estimate.ipynb b/book/estimation/Maximum_Likelihood_Estimate.ipynb index e3a8f55..91ebcc3 100644 --- a/book/estimation/Maximum_Likelihood_Estimate.ipynb +++ b/book/estimation/Maximum_Likelihood_Estimate.ipynb @@ -98,6 +98,7 @@ " # log likelihood for lambda\n", " logL = np.log(likelihood)\n", "\n", + " # plot the likelihood and log-likelihood\n", " plt.figure(figsize=(15, 6))\n", " plt.subplot(1, 2, 1)\n", " plt.plot(lambdas, likelihood)\n", @@ -157,10 +158,12 @@ "outputs": [], "source": [ "def update_plot_mu(n, mu_val):\n", + " # sample from the normal distribution\n", " rng = np.random.default_rng()\n", " data_norm = rng.normal(loc=mu_val, scale=2.0, size=n)\n", " mu = np.linspace(-2, 5, 200)\n", "\n", + " # calculate the likelihood for mu\n", " likelihood = []\n", " logL_mu = []\n", "\n", @@ -178,6 +181,7 @@ " likelihood = np.array(likelihood)\n", " logL_mu = np.array(logL_mu)\n", "\n", + " # plot the likelihood and log-likelihood\n", " plt.figure(figsize=(15, 6))\n", " plt.subplot(1, 2, 1)\n", " plt.plot(mu, likelihood)\n", @@ -223,10 +227,12 @@ "outputs": [], "source": [ "def update_plot_sigma(n, sigma_val):\n", + " # sample from the normal distribution\n", " rng = np.random.default_rng()\n", " data_norm = rng.normal(loc=1.0, scale=sigma_val, size=n)\n", " sigma = np.linspace(0.1, 5, 200)\n", "\n", + " # calculate the likelihood for sigma\n", " # mle of mu\n", " mu0 = data_norm.mean()\n", " likelihood = []\n", @@ -237,7 +243,7 @@ " for s in sigma:\n", " log_likelihood = (\n", " -n / 2 * np.log(2 * np.pi)\n", - " - n / 2 * np.log(s**2)\n", + " - n * np.log(s)\n", " - sum_squared_deviations / (2 * s**2)\n", " )\n", " logL_sigma.append(log_likelihood)\n", @@ -247,6 +253,7 @@ " likelihood = np.array(likelihood)\n", " logL_sigma = np.array(logL_sigma)\n", "\n", + " # plot the likelihood and log-likelihood\n", " plt.figure(figsize=(15, 6))\n", " plt.subplot(1, 2, 1)\n", " plt.plot(sigma, likelihood)\n", @@ -312,7 +319,57 @@ "metadata": {}, "outputs": [], "source": [ - "# add code for 3d plot" + "def update_plot_3d(n, mu_val, sigma_val):\n", + " # sample from the normal distribution\n", + " rng = np.random.default_rng()\n", + " data_norm = rng.normal(loc=mu_val, scale=sigma_val, size=n)\n", + " mu = np.linspace(-1, 3, 200)\n", + " sigma = np.linspace(0.1, 5, 200)\n", + "\n", + " # calculate the likelihood for mu and sigma\n", + " MU, SIGMA = np.meshgrid(mu, sigma)\n", + "\n", + " likelihood = np.zeros((len(mu), len(sigma)))\n", + " logL_mu_sigma = np.zeros((len(mu), len(sigma)))\n", + "\n", + " for i, m in enumerate(mu):\n", + " for j, s in enumerate(sigma):\n", + " log_likelihood = (\n", + " -n / 2 * np.log(2 * np.pi)\n", + " - n / 2 * np.log(s**2)\n", + " - np.sum((data_norm - m) ** 2) / (2 * s**2)\n", + " )\n", + " logL_mu_sigma[i, j] = log_likelihood\n", + " likelihood[i, j] = np.exp(log_likelihood)\n", + "\n", + " # plot the likelihood and log-likelihood\n", + " fig = plt.figure(figsize=(15, 6))\n", + " ax1 = fig.add_subplot(121, projection=\"3d\")\n", + " ax1.plot_surface(MU, SIGMA, likelihood.T, alpha=0.8, cmap=\"viridis\")\n", + " ax1.set_title(\"Likelihood Surface\")\n", + " ax1.set_xlabel(\"$\\mu$\")\n", + " ax1.set_ylabel(\"$\\sigma$\")\n", + " ax1.set_zlabel(\"$L(\\mu, \\sigma)$\")\n", + "\n", + " ax2 = fig.add_subplot(122, projection=\"3d\")\n", + " ax2.plot_surface(MU, SIGMA, logL_mu_sigma.T, alpha=0.8, cmap=\"viridis\")\n", + " ax2.set_title(\"Log-Likelihood Surface\")\n", + " ax2.set_xlabel(\"$\\mu$\")\n", + " ax2.set_ylabel(\"$\\sigma$\")\n", + " ax2.set_zlabel(\"$\\ln L(\\mu, \\sigma)$\")\n", + "\n", + " plt.show()\n", + "\n", + "\n", + "sigma_true = 2.0\n", + "mu_true = 1.0\n", + "slider_n = widgets.IntSlider(\n", + " value=50, min=10, max=200, step=10, description=\"Sample Size\"\n", + ")\n", + "interactive_plot = interactive(\n", + " update_plot_3d, n=slider_n, mu_val=fixed(mu_true), sigma_val=fixed(sigma_true)\n", + ")\n", + "interactive_plot" ] }, { @@ -353,10 +410,11 @@ "outputs": [], "source": [ "def update_plot_exp(sample_size, lambda_val):\n", - "\n", + " # sample from the exponential distribution\n", " rng = np.random.default_rng()\n", " data_exp = rng.exponential(scale=1 / lambda_val, size=sample_size)\n", "\n", + " # calculate the likelihood for lambda\n", " lambdas = np.linspace(0.1, 10, 500)\n", " n = data_exp.size\n", " sum_x = data_exp.sum()\n", @@ -367,6 +425,7 @@ " # log likelihood for lambda\n", " logL = -n * np.log(lambdas) - sum_x / lambdas\n", "\n", + " # plot the likelihood and log-likelihood\n", " plt.figure(figsize=(15, 6))\n", " plt.subplot(1, 2, 1)\n", " plt.plot(lambdas, likelihood)\n", diff --git a/book/estimation/ecdf_kde.ipynb b/book/estimation/ecdf_kde.ipynb index 008aafa..0b43d23 100644 --- a/book/estimation/ecdf_kde.ipynb +++ b/book/estimation/ecdf_kde.ipynb @@ -58,10 +58,13 @@ "outputs": [], "source": [ "def update_histogram(mu_val, scale_val, sample_size):\n", + " # sample from the normal distribution\n", " rng = np.random.default_rng()\n", " data_norm = rng.normal(loc=mu_val, scale=scale_val, size=sample_size)\n", " x = np.linspace(mu_val - 4 * scale_val, mu_val + 4 * scale_val, 1000)\n", - " plt.figure()\n", + "\n", + " # plot the histogram and theoretical normal distribution\n", + " plt.figure(figsize=(12, 6))\n", " plt.plot(\n", " x,\n", " norm.pdf(x, loc=mu_val, scale=scale_val),\n", @@ -108,6 +111,7 @@ "outputs": [], "source": [ "def update_ecdf(mu_val, scale_val, sample_size):\n", + " # sample from the normal distribution\n", " rng = np.random.default_rng()\n", " x = np.linspace(mu_val - 4 * scale_val, mu_val + 4 * scale_val, 1000)\n", "\n", @@ -117,7 +121,8 @@ "\n", " actual_cdf = norm.cdf(x, loc=mu_val, scale=scale_val)\n", "\n", - " plt.figure()\n", + " # plot the ECDF and theoretical normal CDF\n", + " plt.figure(figsize=(12, 6))\n", " plt.plot(x, actual_cdf, \"b-\", linewidth=2, label=\"Theoretical Normal CDF\")\n", "\n", " plt.plot(x, ecdf_y, \"r-\", label=\"Sample ECDF\")\n", @@ -161,12 +166,14 @@ "outputs": [], "source": [ "def update_kde(mu_val, scale_val, sample_size):\n", + " # sample from the normal distribution\n", " rng = np.random.default_rng()\n", " x = np.linspace(mu_val - 4 * scale_val, mu_val + 4 * scale_val, 1000)\n", "\n", " data_norm = rng.normal(loc=mu_val, scale=scale_val, size=sample_size)\n", "\n", - " plt.figure()\n", + " # plot the KDE and theoretical normal distribution\n", + " plt.figure(figsize=(12, 6))\n", " plt.hist(data_norm, density=True)\n", " sns.kdeplot(data_norm, fill=True, label=\"Sample KDE\", color=\"skyblue\", alpha=0.7)\n", " plt.plot(\n", @@ -179,7 +186,7 @@ " )\n", " plt.xlabel(\"$X$\")\n", " plt.ylabel(\"Density\")\n", - " plt.legend()\n", + " plt.legend(loc=\"upper right\")\n", "\n", "\n", "mu_true = 1.0\n", @@ -212,10 +219,13 @@ "outputs": [], "source": [ "def update_exp(lambda_val, sample_size):\n", + " # sample from the exponential distribution\n", " rng = np.random.default_rng()\n", " x = np.linspace(0, 1 / lambda_val * 5, 1000)\n", "\n", " data_norm = rng.exponential(scale=1 / lambda_val, size=sample_size)\n", + "\n", + " # plot the histogram, ECDF, and KDE of the exponential distribution\n", " plt.figure(figsize=(21, 5))\n", "\n", " plt.subplot(1, 3, 1)\n", @@ -287,13 +297,15 @@ "outputs": [], "source": [ "def update_binomial(n, p, sample_size):\n", + " # sample from the binomial distribution\n", " r_vals = np.arange(n + 1)\n", " dist_binomial = binom.pmf(r_vals, n, p)\n", " rng = np.random.default_rng()\n", " sample = rng.binomial(n, p, size=sample_size)\n", " freqs = np.bincount(sample.astype(np.int32), minlength=n + 1) / sample_size\n", "\n", - " plt.figure(figsize=(8, 5))\n", + " # plot the sampled frequency and theoretical PMF\n", + " plt.figure(figsize=(12, 6))\n", " plt.bar(r_vals, freqs, alpha=0.5, label=\"Sampled Frequency\", color=\"orange\")\n", " plt.plot(r_vals, dist_binomial, \"b-\", label=\"Theoretical PMF\", linewidth=2)\n", " plt.xlabel(\"r\")\n", @@ -330,15 +342,16 @@ "outputs": [], "source": [ "def update_ecdf_binomial(n, p, sample_size):\n", + " # sample from the binomial distribution\n", " x = np.arange(n + 1)\n", - "\n", " data_norm = np.random.binomial(n, p, size=sample_size)\n", " ecdf_data = ecdf(data_norm)\n", " ecdf_y = ecdf_data.cdf.evaluate(x)\n", "\n", " actual_cdf = binom.cdf(x, n=n, p=p)\n", "\n", - " plt.figure()\n", + " # plot the ECDF and theoretical binomial CDF\n", + " plt.figure(figsize=(12, 6))\n", " plt.plot(x, actual_cdf, \"b-\", linewidth=2, label=\"Theoretical Binomial CDF\")\n", "\n", " plt.plot(x, ecdf_y, \"r-\", label=\"Sample ECDF\")\n", @@ -376,6 +389,7 @@ "outputs": [], "source": [ "def update_binomial(n, p, sample_size):\n", + " # sample from the binomial distribution\n", " r_vals = np.arange(n + 1)\n", " dist_binomial = binom.pmf(r_vals, n, p)\n", "\n", @@ -383,7 +397,8 @@ " sample = rng.binomial(n, p, size=sample_size)\n", " freqs = np.bincount(sample.astype(np.int32), minlength=n + 1) / sample_size\n", "\n", - " plt.figure(figsize=(8, 5))\n", + " # plot the sampled frequency and theoretical PMF\n", + " plt.figure(figsize=(12, 6))\n", " plt.bar(r_vals, freqs, alpha=0.5, label=\"Sampled Frequency\", color=\"orange\")\n", " plt.plot(r_vals, dist_binomial, \"b-\", label=\"Theoretical PMF\", linewidth=2)\n", " sns.kdeplot(sample, fill=True, label=\"Sample KDE\", color=\"skyblue\", alpha=0.7)\n",