{"id":13444433,"url":"https://github.com/dimenwarper/awesome-bayes","last_synced_at":"2025-03-20T18:32:34.234Z","repository":{"id":49791653,"uuid":"190289989","full_name":"dimenwarper/awesome-bayes","owner":"dimenwarper","description":"List of resources for bayesian inference","archived":false,"fork":false,"pushed_at":"2019-06-17T16:20:32.000Z","size":51,"stargazers_count":150,"open_issues_count":0,"forks_count":23,"subscribers_count":13,"default_branch":"master","last_synced_at":"2024-05-23T04:00:50.592Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dimenwarper.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-06-04T22:49:33.000Z","updated_at":"2024-05-16T11:36:22.000Z","dependencies_parsed_at":"2022-09-13T13:13:12.076Z","dependency_job_id":null,"html_url":"https://github.com/dimenwarper/awesome-bayes","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dimenwarper%2Fawesome-bayes","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dimenwarper%2Fawesome-bayes/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dimenwarper%2Fawesome-bayes/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dimenwarper%2Fawesome-bayes/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dimenwarper","download_url":"https://codeload.github.com/dimenwarper/awesome-bayes/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":220810117,"owners_count":16706796,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-07-31T04:00:22.850Z","updated_at":"2024-10-28T06:30:56.379Z","avatar_url":"https://github.com/dimenwarper.png","language":null,"funding_links":["https://www.patreon.com/twiecki"],"categories":["Uncategorized","Other Lists","Data Analysis"],"sub_categories":["Uncategorized","TeX Lists"],"readme":"# Awesome Bayes\nList of resources for bayesian inference\n\n## Books\n\n* [Statistical Rethinking](https://xcelab.net/rm/statistical-rethinking/)\n* [Bayesian Data Analysis](http://www.stat.columbia.edu/~gelman/book/)\n* [Probability Theory: The Logic of Science](https://www.amazon.com/Probability-Theory-E-T-Jaynes/dp/0521592712/ref=as_li_ss_tl?ie=UTF8\u0026qid=1462140419\u0026sr=8-1\u0026keywords=probability+theory+the+logic+science\u0026linkCode=sl1\u0026tag=counbaye09-20\u0026linkId=c8a7186d02be8069fd78b71cce57b3c0)\n* [The Bayesian Choice](https://www.amazon.com/Bayesian-Choice-Decision-Theoretic-Computational-Implementation/dp/0387715983)\n* [Bayesian methods for hackers](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/)\n* [Bayesian Analysis with Python](https://www.amazon.com/gp/product/1789341655/ref=dbs_a_def_rwt_bibl_vppi_i0) | [errata and extra material](https://github.com/aloctavodia/BAP)\n\n\n## Software/packages\n\n### General inference\n* [BUGS](http://www.openbugs.net/w/FrontPage): Bayesian Inference Using Gibbs Sampling. Oldest of the Bayesian inference platforms, tried and tested. Has a Windows friendly version, WinBUGS. R, python and many other language bindings, GUIs and \n* [JAGS](http://mcmc-jags.sourceforge.net/): Just another Gibbs sampler, similar to BUGS - focused on cross-platform, usability. Also tried and tested. R and python bindings too.\n* [Stan](https://mc-stan.org/): Full-featured Bayesian inference with R and python bindings. Based on Hamiltonian MC and NUTS. Current favorite of the community it seems with lots of examples, docs.\n* [PyMC3](https://docs.pymc.io/): Probabilistic programming in Python/Theano. PyMC4 is in dev, will use Tensorflow as backend. Great API and interface, but hindered by Theano's deprecation. PYMC4 promises great things.\n* [edward2/tfprobability](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/python/edward2): Probabilistic programming in tensorflow. Scalable models, but little docs.\n* [Zhusuan](https://zhusuan.readthedocs.io/en/latest/): Another probabilistic programming framework built on tensorflow.\n* [Pyro](https://pyro.ai/): Probabilistic programming in Pytorch. Good docs, scalable models too.\n* [Brancher](https://brancher.org/): Probabilistic inference based on auto diff and variational models, also based on Pytorch.\n* [LaplacesDemon](https://cran.r-project.org/web/packages/LaplacesDemon/index.html): Mysterious probabilistic programming package in R with a cult following.\n* [WebPPL](http://webppl.org/): Probabilistic programming in the browser.\n* [Turing.jl](https://github.com/TuringLang/Turing.jl): Probabilistic programming in Julia, by Zoubin Ghahramani's lab.\n* [Infer.NET](https://github.com/dotnet/infer): Specializes in running probabilistic inference in factor graphs (Expectation Propagation, Variational Inference). Programs written in .NET.\n\n### Specific\n* [brms](https://github.com/paul-buerkner/brms) : Generalized linear/non-linear multilevel models, uses Stan.\n* [R-INLA](http://www.r-inla.org/) : Latent Gaussian models via Integrated Nested Latent Approximations. Really fast compared to MCMC.\n* [bayesmix](https://cran.r-project.org/web/packages/bayesmix/index.html): Finite mixture models with JAGS in R\n* [lmm](https://cran.r-project.org/web/packages/lmm/index.html): Linear mixed models fitted with MCMC\n\n### Misc\n* [List of Bayesian inference packages for R](https://cran.r-project.org/web/views/Bayesian.html): Comprehensive list for all Bayesian inference in R\n* [ArviZ](https://arviz-devs.github.io/arviz/): ArviZ is a Python package for exploratory analysis of Bayesian models. Includes functions for posterior analysis, sample diagnostics, model checking, and comparison. Works with PyMC3, PyStan, emcee, Pyro and TensorFlow Probability.\n* [StatSim](https://statsim.com/): Browser-based interface to create, share, and perform inference on probabilistic models. Powered by WebPPL and PyMC3.\n\n## Resources, papers, and blogs\n\n### General topics\n* [Michael Clark's Blog on Stat modeling, Bayesian inference](https://m-clark.github.io/documents.html)\n* [Bayesian Spectacles](https://www.bayesianspectacles.org/)\n\n\n### Introductory\n* [Count Bayesie](https://www.countbayesie.com/all-posts): Will Kurt from \"Get Programming with Haskell\" fame explains basic probability and stats concepts through a Bayesian lens in a fun way. \n* [How to become a Bayesian in eight easy steps](https://link.springer.com/article/10.3758/s13423-017-1317-5)\n* [Introduction to Bayesian Statistics](https://www.stat.auckland.ac.nz/~brewer/stats331.pdf): Course lectures by Brendon Brewer (University of Auckland)\n* [Michael Jordan's Bayesian Statistics Course Notes](https://people.eecs.berkeley.edu/~jordan/courses/260-spring10/lectures/index.html)\n\n### MCMC\n* [The MCMC interactive gallery](https://chi-feng.github.io/mcmc-demo/): Build intuition for common MCMC routines using interactive demos. A walkthrough of the demos can be found [here](http://elevanth.org/blog/2017/11/28/build-a-better-markov-chain/).\n* [The MCMC handbook intro to MCMC](https://www.mcmchandbook.net/HandbookChapter1.pdf): A no-frills intro to MCMC\n* [Scaling up Bayesian inference](https://simons.berkeley.edu/sites/default/files/docs/6625/dunsonsimons2017.pdf): For Big Data™\n\n### Variational Inference\n* [Intro to variational inference via mean field approx](https://blog.evjang.com/2016/08/variational-bayes.html)\n* [David Blei's Variational Inference tutorial](https://www.cs.princeton.edu/courses/archive/fall11/cos597C/lectures/variational-inference-i.pdf)\n* [Expectation Maximization and Variational Inference](https://chrischoy.github.io/research/Expectation-Maximization-and-Variational-Inference/) \n\n### Empirical Bayes\n* [Understanding Empirical Bayes](http://varianceexplained.org/r/empirical_bayes_baseball/)\n* [Efron's overview of Empirical Bayes](http://statweb.stanford.edu/~ckirby/brad/papers/2013EBModeling.pdf)\n\n### Non-parametrics\n* [Collection of tutorials on non-parametrics](http://stat.columbia.edu/~porbanz/npb-tutorial.html)\n* [Infinite mixture models](https://blog.echen.me/2012/03/20/infinite-mixture-models-with-nonparametric-bayes-and-the-dirichlet-process/)\n* [A Visual Exploration of Gaussian Processes](https://distill.pub/2019/visual-exploration-gaussian-processes/)\n\n### INLA\n* [A gentle INLA tutorial](https://www.precision-analytics.ca/blog-1/inla)\n* [Step by step INLA tutorial](http://www.flutterbys.com.au/stats/tut/tut12.9.html)\n\n### Bayesian deep learning\n* [Yarin Gal's talk on Bayesian Deep Learning](https://www.cs.ox.ac.uk/people/yarin.gal/website/PDFs/2017_OReilly_talk.pdf): Blog post of the talk is also very informative, check it out [here](http://mlg.eng.cam.ac.uk/yarin/blog_3d801aa532c1ce.html)\n* [What is a variational autoencoder?](https://jaan.io/what-is-variational-autoencoder-vae-tutorial/)\n\n### Misc\n* [Probabilistic Numerics](http://probabilistic-numerics.org/): View all numeric optimization through a Bayesian lens\n\n### Michael Betancourt's case studies\n\nIndexed [here](https://betanalpha.github.io/writing/), these deserve a list all to themselves:\n* [Principled Bayesian Worflow](https://betanalpha.github.io/assets/case_studies/principled_bayesian_workflow.html): What to know and what to look for when doing Bayesian inference.\n* [Conceptual introduction to Hamiltonian MC](https://arxiv.org/pdf/1701.02434.pdf)\n* [Identifying Bayesian Mixture Models](https://betanalpha.github.io/assets/case_studies/identifying_mixture_models.html)\n* [Diagnosing Biased Inference Using Divergences](https://betanalpha.github.io/assets/case_studies/divergences_and_bias.html)\n\n\n## People\n\n* [Michael Jordan](https://people.eecs.berkeley.edu/~jordan/)\n* [Zoubin Ghahramani](http://mlg.eng.cam.ac.uk/zoubin/)\n* [Danielle Navarro](https://compcogscisydney.org/)\n* [Michael Betancourt](https://betanalpha.github.io/)\n* [Dan Simpson](https://twitter.com/dan_p_simpson?lang=en)\n* [Thomas Wiecki](https://www.patreon.com/twiecki)\n* [David Blei](http://www.cs.columbia.edu/~blei/)\n* [Andrew Gelman](http://www.stat.columbia.edu/~gelman/)\n* [Dustin Tran](http://dustintran.com/)\n* [Yarin Gal](http://www.cs.ox.ac.uk/people/yarin.gal/website/)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdimenwarper%2Fawesome-bayes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdimenwarper%2Fawesome-bayes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdimenwarper%2Fawesome-bayes/lists"}