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.. image:: https://cdn.rawgit.com/pymc-devs/pymc3/master/docs/logos/svg/PyMC3_banner.svg
:height: 100px
:alt: PyMC3 logo
:align: center

PyMC3 Examples
==============

Supporting examples and tutorials for PyMC3, the Python package for Bayesian statistical modeling and Probabilistic Machine Learning!

Check out the `getting started guide <http://docs.pymc.io/notebooks/getting_started>`__, or
`interact with live examples <https://mybinder.org/v2/gh/pymc-devs/pymc3/master?filepath=%2Fdocs%2Fsource%2Fnotebooks>`__
using Binder!
For questions on PyMC3, head on over to our `PyMC Discourse <https://discourse.pymc.io/>`__ forum.

Getting started
===============

If you already know about Bayesian statistics:
----------------------------------------------


- `API quickstart guide <http://docs.pymc.io/notebooks/api_quickstart>`__
- The `PyMC3 tutorial <http://docs.pymc.io/notebooks/getting_started>`__
- `PyMC3 examples <https://docs.pymc.io/nb_examples/index.html>`__ and the `API reference <http://docs.pymc.io/api>`__



Learn Bayesian statistics with a book together with PyMC3:
----------------------------------------------------------

- `Probabilistic Programming and Bayesian Methods for Hackers <https://github.com/CamDavidsonPilon/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 <https://github.com/aloctavodia/Doing_bayesian_data_analysis>`__ as well as the `second edition <https://github.com/JWarmenhoven/DBDA-python>`__: 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 <https://github.com/pymc-devs/resources/tree/master/Rethinking>`__
- `PyMC3 port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers <https://github.com/pymc-devs/resources/tree/master/BCM>`__: 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).

PyMC3 talks
-----------

There are also several talks on PyMC3 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 PyMC3 on your system, see its `installation section here <https://github.com/pymc-devs/pymc3#installation>`__

Citing PyMC3
============

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>`__.

Papers citing PyMC3
-------------------

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

Contributors
============

See the `GitHub contributor
page <https://github.com/pymc-devs/pymc-examples/graphs/contributors>`__. Also read our `Code of Conduct <https://github.com/pymc-devs/pymc3/blob/master/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 PyMC3 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:

- `PyMC3 Examples - Issue Tracker <https://github.com/pymc-devs/pymc-examples/issues>`__.
- `PyMC3 - Issue Tracker <https://github.com/pymc-devs/pymc3/issues>`__.

Finally, if you need to get in touch for non-technical information about the project, `send us an e-mail <[email protected]>`__.

Support
=======

PyMC3 is a non-profit project under NumFOCUS umbrella. If you want to support PyMC3 financially, you can donate `here <https://numfocus.salsalabs.org/donate-to-pymc3/index.html>`__.

PyMC for enterprise
===================
`PyMC is now available as part of the Tidelift Subscription!`

Tidelift is working with PyMC and the maintainers of thousands of other open source
projects to deliver commercial support and maintenance for the open source dependencies
you use to build your applications. Save time, reduce risk, and improve code health,
while contributing financially to PyMC -- making it even more robust, reliable and,
let's face it, amazing!

|tidelift_learn| |tidelift_demo|

Sponsors
========

|NumFOCUS|

|Quantopian|

|ODSC|

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