# pymc-examples

**Repository Path**: mirrors_pymc-devs/pymc-examples

## Basic Information

- **Project Name**: pymc-examples
- **Description**: Examples of PyMC models, including a library of Jupyter notebooks.
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2020-12-16
- **Last Updated**: 2026-02-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

.. image:: https://cdn.rawgit.com/pymc-devs/pymc/main/docs/logos/svg/PyMC_banner.svg
    :height: 100px
    :alt: PyMC logo
    :align: center

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 a 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   For issues about the example notebooks, errors in the example codes, and outdated 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
============

Please choose from the following:

- |DOIpaper| *PyMC: A Modern and Comprehensive Probabilistic Programming Framework in Python*, Abril-Pla O, Andreani V, Carroll C, Dong L, Fonnesbeck CJ, Kochurov M, Kumar R, Lao J, Luhmann CC, Martin OA, Osthege M, Vieira R, Wiecki T, Zinkov R. (2023)

  - BibTex version

    .. code:: bibtex

       @article{pymc2023,
         title = {{PyMC}: A Modern and Comprehensive Probabilistic Programming Framework in {P}ython},
         author = {Oriol Abril-Pla and Virgile Andreani and Colin Carroll and Larry Dong and Christopher J. Fonnesbeck and Maxim Kochurov and Ravin Kumar and Junpeng Lao and Christian C. Luhmann and Osvaldo A. Martin and Michael Osthege and Ricardo Vieira and Thomas Wiecki and Robert Zinkov },
         journal = {{PeerJ} Computer Science},
         volume = {9},
         number = {e1516},
         doi = {10.7717/peerj-cs.1516},
         year = {2023}
       }

- |DOIzenodo| A DOI for all versions. DOIs for specific versions are shown on Zenodo and under `Releases <https://github.com/pymc-devs/pymc/releases>`_

.. |DOIpaper| image:: https://img.shields.io/badge/DOI-10.7717%2Fpeerj--cs.1516-blue.svg
     :target: https://doi.org/10.7717/peerj-cs.1516
.. |DOIzenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4603970.svg
   :target: https://doi.org/10.5281/zenodo.4603970

- 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>`__.


Sponsors
========

|NumFOCUS|

|PyMCLabs|

.. |zenodo| image:: https://zenodo.org/badge/321449673.svg
   :target: https://zenodo.org/badge/latestdoi/321449673
.. |NumFOCUS| image:: https://www.numfocus.org/wp-content/uploads/2017/03/1457562110.png
   :target: http://www.numfocus.org/
.. |PyMCLabs| image:: https://raw.githubusercontent.com/pymc-devs/pymc/main/docs/logos/sponsors/pymc-labs.png
   :target: https://pymc-labs.io