Package: pymc-nightly
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,  orinteract with live examplesusing Binder!
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 onAesarawhich 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 thesecond edition: Principled introduction to Bayesian data analysis.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 the appropriate installation guide:Installing PyMC on MacOSInstalling PyMC on LinuxInstalling PyMC on WindowsCiting PyMCPlease choose from the following:Probabilistic programming in Python using PyMC3, Salvatier J., Wiecki T.V., Fonnesbeck C. (2016)A DOI for all versions.DOIs for specific versions are shown on Zenodo and underReleasesContactWe 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 PyMC 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 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.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 APIfenics-pymc3: Differentiable interface to FEniCS, a library for solving partial differential equations.Domain specificExoplanet: a toolkit for modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series.NiPyMC: Bayesian mixed-effects modeling of fMRI data in Python.beat: Bayesian Earthquake Analysis Tool.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 PyMCSeeGoogle Scholarfor 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