Package: pymc
PyMC (formerly PyMC3) is a Python package for Bayesian statistical modeling
focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI)
algorithms. Its flexibility and extensibility make it applicable to a
large suite of problems.Check out thePyMC overview,  or
one ofthe many examples!
For questions on PyMC, head on over to ourPyMC Discourseforum.FeaturesIntuitive model specification syntax, for example,x ~ N(0,1)translates tox =Normal('x',0,1)Powerful sampling algorithms, such as theNo U-Turn
Sampler, allow complex models
with thousands of parameters with little specialized knowledge of
fitting algorithms.Variational inference:ADVIfor fast approximate posterior estimation as well as mini-batch ADVI
for large data sets.Relies onPyTensorwhich provides:Computation optimization and dynamic C or JAX compilationNumPy broadcasting and advanced indexingLinear algebra operatorsSimple extensibilityTransparent support for missing value imputationGetting startedIf you already know about Bayesian statistics:API quickstart guideThePyMC tutorialPyMC examplesand theAPI referenceLearn Bayesian statistics with a book together with PyMCProbabilistic Programming and Bayesian Methods for Hackers: Fantastic book with many applied code examples.PyMC port of the book “Doing Bayesian Data Analysis” by John Kruschkeas well as thefirst edition.PyMC port of the book “Statistical Rethinking A Bayesian Course with Examples in R and Stan” by Richard McElreathPyMC port of the book “Bayesian Cognitive Modeling” by Michael Lee and EJ Wagenmakers: Focused on using Bayesian statistics in cognitive modeling.Bayesian Analysis with Python(second edition) by Osvaldo Martin: Great introductory book. (codeand errata).Audio & VideoHere is aYouTube playlistgathering several talks on PyMC.You can also find all the talks given atPyMCon 2020here.The“Learning Bayesian Statistics” podcasthelps you discover and stay up-to-date with the vast Bayesian community. Bonus: it’s hosted by Alex Andorra, one of the PyMC core devs!InstallationTo install PyMC on your system, follow the instructions on theinstallation guide.Citing PyMCPlease choose from the following:PyMC: A Modern and Comprehensive Probabilistic Programming Framework in Python, Abril-Pla O, Andreani V, Carroll C, Dong L, Fonnesbeck CJ, Kochurov M, Kumar R, Lao J, Luhmann CC, Martin OA, Osthege M, Vieira R, Wiecki T, Zinkov R. (2023)A DOI for all versions.DOIs for specific versions are shown on Zenodo and underReleasesContactWe are usingdiscourse.pymc.ioas our main communication channel.To ask a question regarding modeling or usage of PyMC we encourage posting to our Discourse forum under the“Questions” Category. You can also suggest feature in the“Development” Category.You can also follow us on these social media platforms for updates and other announcements:LinkedIn @pymcYouTube @PyMCDevelopersTwitter @pymc_devsMastodon @pymc@bayes.clubTo report an issue with PyMC please use theissue tracker.Finally, if you need to get in touch for non-technical information about the project,send us an e-mail.LicenseApache License, Version
2.0Software using PyMCGeneral purposeBambi: BAyesian Model-Building Interface (BAMBI) in Python.calibr8: A toolbox for constructing detailed observation models to be used as likelihoods in PyMC.gumbi: A high-level interface for building GP models.SunODE: Fast ODE solver, much faster than the one that comes with PyMC.pymc-learn: Custom PyMC models built on top of pymc3_models/scikit-learn APIDomain specificExoplanet: a toolkit for modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series.beat: Bayesian Earthquake Analysis Tool.CausalPy: A package focussing on causal inference in quasi-experimental settings.Please contact us if your software is not listed here.Papers citing PyMCSee Google Scholarhereandherefor a continuously updated list.ContributorsSee theGitHub contributor
page. Also read ourCode of Conductguidelines for a better contributing experience.SupportPyMC is a non-profit project under NumFOCUS umbrella. If you want to support PyMC financially, you can donatehere.Professional Consulting SupportYou can get professional consulting support fromPyMC Labs.Sponsors