diff --git a/CHANGELOG.md b/CHANGELOG.md index 0647995..af05bb3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,8 @@ The format follows [Keep a Changelog](https://keepachangelog.com/en/1.1.0/). ### Added +- Software archive DOI and method references for flow preconditioning and + masked autoregressive flows. - Documentation sidebar links to the main Sequince documentation and sequince-examples under Related Packages. diff --git a/docs/index.rst b/docs/index.rst index b3e5437..73206a3 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -36,6 +36,21 @@ Features sequince sequince-examples +Citing sequince-density +----------------------- + +If you use ``sequince-density``, please cite the archived software: + +Williams, M. J. (2026). *sequince-dev/sequince-density*. +Zenodo. `doi:10.5281/zenodo.23687687 `_. + +Ideally, cite the Zenodo record for the exact version used in your analysis. +Select that version on Zenodo and export its citation in your preferred +format, using that record's DOI. Also cite the methods and +backends used in your analysis; see :doc:`normalizing_flows` and :doc:`gaussian_mixtures`. +For the sampling framework, follow `Sequince's citation guidelines +`_. + Related projects ---------------- diff --git a/docs/normalizing_flows.rst b/docs/normalizing_flows.rst index 32828b4..94ba6ad 100644 --- a/docs/normalizing_flows.rst +++ b/docs/normalizing_flows.rst @@ -27,6 +27,13 @@ evaluate their log densities: ``ZukoFlow`` returns PyTorch tensors; ``FlowJax`` returns JAX arrays. +The ``MAF`` architecture used here is the masked autoregressive flow of +`Papamakarios, Pavlakou, and Murray (2017) +`_, *Masked Autoregressive Flow for Density +Estimation*. Credit the architecture you select as well as the backend +implementation: `Zuko `_ or +`FlowJAX `_. + Particle transform restrictions ------------------------------- @@ -43,6 +50,12 @@ Nonperiodic transforms, such as ``AffineTransform``, remain supported. Preconditioned sampling ----------------------- +Flow-preconditioned SMC is described by `Karamanis et al. (2022) +`_, *Accelerating astronomical and +cosmological inference with Preconditioned Monte Carlo*. This is the method +paper; the separate `pocoMC software paper +`_ describes that package. + For preconditioned SMC, pass an unfitted flow to ``FlowTransform`` and use the transform as the ``particle_transform`` argument to ``make_standard_bayesian_smc``. The mutation step fits it to the current particle