Supporting examples and tutorials for PyMC3, the Python package for Bayesian statistical modeling and Probabilistic Machine Learning!
Check out the getting started guide, or interact with live examples using Binder! For questions on PyMC3, 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 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, please use the following:
issues about the example notebooks, errors in the example codes, outdated information, improvement suggestions...
- PyMC3 - Issue Tracker. For issues, bugs or
feature requests related to the PyMC3 library itself.
Finally, if you need to get in touch for non-technical information about the project, send us an e-mail. Getting started ===============
- 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 Kruschke as well as the second 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 McElreath
- PyMC3 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. (code and errata).
There are also several talks on PyMC3 which are gathered in this YouTube playlist and as part of PyMCon 2020
To install PyMC3 on your system, see its installation section here
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.
See Google Scholar for a continuously updated list.
PyMC3 is a non-profit project under NumFOCUS umbrella. If you want to support PyMC3 financially, you can donate here.
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