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PyMC Examples

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

Check out the getting started guide <https://www.pymc.io/projects/docs/en/latest/learn.html>, or interact with live examples using Binder! Each notebook in PyMC examples gallery <https://www.pymc.io/projects/examples/en/latest/gallery.html> has a binder badge. For questions on PyMC, head on over to our PyMC Discourse <https://discourse.pymc.io/>__ forum.

Contributing

If you are interested in contributing to the example notebooks hosted here, please read the contributing guide <https://github.com/pymc-devs/pymc-examples/blob/main/CONTRIBUTING.md> Also read our Code of Conduct <https://github.com/pymc-devs/pymc-examples/blob/main/CODE_OF_CONDUCT.md> guidelines for a better contributing experience.

Contact

We are using discourse.pymc.io <https://discourse.pymc.io/> as our main communication channel. You can also follow us on Twitter @pymc_devs <https://twitter.com/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 <https://discourse.pymc.io/c/questions>. You can also suggest feature in the “Development” Category <https://discourse.pymc.io/c/development>.

To report an issue, please use the following:

PyMC Examples - Issue Tracker <https://github.com/pymc-devs/pymc-examples/issues>__. For issues about the example notebooks, errors in the example codes, outdated information, improvement suggestions...
PyMC - Issue Tracker <https://github.com/pymc-devs/pymc/issues>__. 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 <pymc.devs@gmail.com>__.

Getting started

If you already know about Bayesian statistics:

API quickstart guide <https://www.pymc.io/projects/examples/en/latest/howto/api_quickstart.html>__
The PyMC tutorial <https://www.pymc.io/projects/docs/en/stable/learn/core_notebooks/pymc_overview.html>__
PyMC examples <https://www.pymc.io/projects/examples/en/latest/gallery.html> and the API reference <https://www.pymc.io/projects/docs/en/stable/api.html>

Learn Bayesian statistics with a book together with PyMC:

Probabilistic Programming and Bayesian Methods for Hackers <https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers>__ by Cameron Davidson-Pilon: Fantastic book with many applied code examples.
Doing Bayesian Data Analysis <https://github.com/aloctavodia/Doing_bayesian_data_analysis> by John Kruschke, as well as the second edition <https://github.com/JWarmenhoven/DBDA-python>: Principled introduction to Bayesian data analysis.
Statistical Rethinking: A Bayesian Course with Examples in R and Stan <https://github.com/pymc-devs/resources/tree/master/Rethinking>__ by Richard McElreath: Comprehensive text on modeling choices and interpretations.
Bayesian Cognitive Modeling <https://github.com/pymc-devs/resources/tree/master/BCM>__ by Michael Lee and EJ Wagenmakers: Focused on using Bayesian statistics in cognitive modeling.
Bayesian Analysis with Python <https://www.packtpub.com/big-data-and-business-intelligence/bayesian-analysis-python-second-edition> (second edition) by Osvaldo Martin: Great introductory book. (code <https://github.com/aloctavodia/BAP> and errata).

PyMC talks

There are also several talks on PyMC which are gathered in this YouTube playlist <https://www.youtube.com/playlist?list=PL1Ma_1DBbE82OVW8Fz_6Ts1oOeyOAiovy> and as part of PyMCon 2020 <https://discourse.pymc.io/c/pymcon/2020talks/15>

Installation

To install PyMC on your system, see its installation section here <https://www.pymc.io/projects/docs/en/stable/installation.html>__

Citing PyMC

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 <https://doi.org/10.7717/peerj-cs.55>__.
To cite specific guides from this collection, use |zenodo|. You'll find page specific citation instructions at the bottom of each page.

Papers citing PyMC

See Google Scholar <https://scholar.google.de/scholar?oi=bibs&hl=en&authuser=1&cites=6936955228135731011>__ for a continuously updated list.

Support

PyMC is a non-profit project under NumFOCUS umbrella. If you want to support PyMC financially, you can donate here <https://numfocus.salsalabs.org/donate-to-pymc3/index.html>__.

pymc-devs / pymc-examples

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