Package: pymc3
PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning
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 thegetting started guide,  orinteract with live examplesusing Binder!
For questions on PyMC3, head on over to ourPyMC Discourseforum.The future of PyMC3 & TheanoThere have been many questions and uncertainty around the future of PyMC3 since Theano
stopped getting developed by the original authors, and we started experiments with a PyMC version based on tensorflow probability.Since then many things changed and we are happy to announce that PyMC3 will continue to rely on Theano,
or rather its successors Theano-PyMC (pymc3 <4)and Aesara (pymc3 >=4).
Check out <https://github.com/aesara-devs/aesara>`__) and specifically the latest developments on thePyMC3 `mainbranch <https://github.com/pymc-devs/pymc3/>`.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 onTheano-PyMCwhich 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 guideThePyMC3 tutorialPyMC3 examplesand theAPI referenceLearn Bayesian statistics with a book together with PyMC3:Probabilistic Programming and Bayesian Methods for Hackers: Fantastic book with many applied code examples.PyMC3 port of the book “Doing Bayesian Data Analysis” by John Kruschkeas well as thesecond edition: Principled introduction to Bayesian data analysis.PyMC3 port of the book “Statistical Rethinking A Bayesian Course with Examples in R and Stan” by Richard McElreathPyMC3 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).PyMC3 talksThere are also several talks on PyMC3 which are gathered in thisYouTube playlistand as part ofPyMCon 2020InstallationTo install PyMC3 on your system, follow the instructions on the appropriate installation guide:Installing PyMC3 on MacOSInstalling PyMC3 on LinuxInstalling PyMC3 on WindowsCiting PyMC3Salvatier J., Wiecki T.V., Fonnesbeck C. (2016) Probabilistic programming
in Python using PyMC3. PeerJ Computer Science 2:e55DOI: 10.7717/peerj-cs.55.ContactWe are usingdiscourse.pymc.ioas our main communication channel. You can also follow us onTwitter @pymc_devsfor updates and other announcements.To ask a question regarding modeling or usage of PyMC3 we encourage posting to our Discourse forum under the“Questions” Category. You can also suggest feature in the“Development” Category.To report an issue with PyMC3 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 PyMC3Exoplanet: a toolkit for modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series.Bambi: BAyesian Model-Building Interface (BAMBI) in Python.pymc3_models: Custom PyMC3 models built on top of the scikit-learn API.PMProphet: PyMC3 port of Facebook’s Prophet model for timeseries modelingwebmc3: A web interface for exploring PyMC3 tracessampled: Decorator for PyMC3 models.NiPyMC: Bayesian mixed-effects modeling of fMRI data in Python.beat: Bayesian Earthquake Analysis Tool.pymc-learn: Custom PyMC models built on top of pymc3_models/scikit-learn APIfenics-pymc3: Differentiable interface to FEniCS, a library for solving partial differential equations.cell2location: Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics.Please contact us if your software is not listed here.Papers citing PyMC3SeeGoogle Scholarfor a continuously updated list.ContributorsSee theGitHub contributor
page. Also read ourCode of Conductguidelines for a better contributing experience.SupportPyMC3 is a non-profit project under NumFOCUS umbrella. If you want to support PyMC3 financially, you can donatehere.PyMC for enterprisePyMC is now available as part of the Tidelift Subscription!Tidelift is working with PyMC and the maintainers of thousands of other open source
projects to deliver commercial support and maintenance for the open source dependencies
you use to build your applications. Save time, reduce risk, and improve code health,
while contributing financially to PyMC – making it even more robust, reliable and,
let’s face it, amazing!Sponsors