BOBE (Bayesian Optimation for Bayesian Evidence) is a package for Bayesian model selection with expensive likelihood functions, developed for applications to cosmology.
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Updated
Jan 22, 2026 - Python
BOBE (Bayesian Optimation for Bayesian Evidence) is a package for Bayesian model selection with expensive likelihood functions, developed for applications to cosmology.
Gaussian Processes for Cyclic Voltammetry
Materials from the graduate course on Generalized Linear Models at SFU in Spring 2020
Regularization, Bayesian Model Selection and k-fold Cross-Validation Selection
A sampling-based implementation of the Bayesian model-selection method of Stephan et al. (2009) in NeuroImage.
This is a group project on predicting painting prices that were sold from 1764 to 1780. Based on our analysis, we identify undervalued/overvalued paintings in the dataset.
A hands-on guide to model selection, emphasizing high-dimensional problems, Bayesian model selection and averaging, and L0 criteria
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