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Supporting examples and tutorials for PyMC, the Python package for Bayesianstatistical modeling and Probabilistic Machine Learning!

Check out thegetting started guide,or interact with live examples using Binder!Each notebook inPyMC examples galleryhas a binder badge.For questions on PyMC, head on over to ourPyMC Discourse forum.

If you are interested in contributing to the example notebooks hosted here,please read thecontributing guideAlso read ourCode of Conductguidelines for a better contributing experience.

We are usingdiscourse.pymc.io as our maincommunication 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 toour Discourse forum under the“Questions” Category.You can also suggest feature in the“Development” Category.

To report an issue, please use the following:

• PyMC Examples - Issue Tracker.For issues about the example notebooks, errors in the example codes, outdatedinformation, improvement suggestions...
• PyMC - Issue Tracker. Forissues, bugs or feature requests related to the PyMC library itself.

Finally, if you need to get in touch for non-technical information about theproject,send us an e-mail.

• API quickstart guide
• ThePyMC tutorial
• PyMC examplesand theAPI reference

• Probabilistic Programming and Bayesian Methods for Hackersby Cameron Davidson-Pilon: Fantastic book with many applied code examples.
• Doing Bayesian Data Analysisby John Kruschke, as well as thesecond edition:Principled introduction to Bayesian data analysis.
• Statistical Rethinking: A Bayesian Course with Examples in R and Stanby Richard McElreath: Comprehensive text on modeling choices and interpretations.
• Bayesian Cognitive Modelingby 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.(code and errata).

There are also several talks on PyMC which are gathered in thisYouTube playlistand as part ofPyMCon 2020

To install PyMC on your system, see itsinstallation section here

• To cite the PyMC software, use:

  Salvatier J., Wiecki T.V., Fonnesbeck C. (2016) Probabilistic programmingin Python using PyMC3. PeerJ Computer Science 2:e55DOI: 10.7717/peerj-cs.55.

• To cite specific guides from this collection, use. You'll find page specificcitation instructions at the bottom of each page.

SeeGoogle Scholarfor a continuously updated list.

PyMC is a non-profit project under NumFOCUS umbrella. If you want to supportPyMC financially, you can donatehere.