Supporting examples and tutorials for PyMC, the Python package for Bayesian statistical modeling and Probabilistic Machine Learning!
Check out the getting started guide, or interact with live examples using Binder! Each notebook in PyMC examples gallery has a binder badge. For questions on PyMC, head on over to our PyMC Discourse forum.
If you are interested in contributing to the example notebooks hosted here, please read the contributing guide Also read our Code of Conduct guidelines for a better contributing experience.
We are using discourse.pymc.io as our main communication channel. You can also follow us on Twitter @pymc_devs for 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, please use the following:
issues about the example notebooks, errors in the example codes, outdated information, improvement suggestions...
- PyMC - Issue Tracker. For issues, bugs or
feature requests related to the PyMC library itself.
Finally, if you need to get in touch for non-technical information about the project, send us an e-mail.
- API quickstart guide
- The PyMC tutorial
- PyMC examples and the API reference
- Probabilistic Programming and Bayesian Methods for Hackers by Cameron Davidson-Pilon: Fantastic book with many applied code examples.
- Doing Bayesian Data Analysis by John Kruschke, as well as the second edition: Principled introduction to Bayesian data analysis.
- Statistical Rethinking: A Bayesian Course with Examples in R and Stan by Richard McElreath: Comprehensive text on modeling choices and interpretations.
- 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. (code and errata).
There are also several talks on PyMC which are gathered in this YouTube playlist and as part of PyMCon 2020
To install PyMC on your system, see its installation section here
To cite the PyMC software, use:
Salvatier J., Wiecki T.V., Fonnesbeck C. (2016) Probabilistic programming in Python using PyMC3. PeerJ Computer Science 2:e55 DOI: 10.7717/peerj-cs.55.
To cite specific guides from this collection, use
. You'll find page specific citation instructions at the bottom of each page.
See Google Scholar for a continuously updated list.
PyMC is a non-profit project under NumFOCUS umbrella. If you want to support PyMC financially, you can donate here.
PyMC is now available as part of the Tidelift Subscription!
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