pymc

https://github.com/jonazz1995/pymc

Repository

README.rst

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

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PyMC (formerly PyMC3) is a Python package for Bayesian statistical modeling
focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI)
algorithms. Its flexibility and extensibility make it applicable to a
large suite of problems.

Check out the `PyMC overview `__,  or
one of `the many examples `__!
For questions on PyMC, head on over to our `PyMC Discourse `__ forum.

Features
========

-  Intuitive model specification syntax, for example, ``x ~ N(0,1)``
   translates to ``x = Normal('x',0,1)``
-  **Powerful sampling algorithms**, such as the `No U-Turn
   Sampler `__, allow complex models
   with thousands of parameters with little specialized knowledge of
   fitting algorithms.
-  **Variational inference**: `ADVI `__
   for fast approximate posterior estimation as well as mini-batch ADVI
   for large data sets.
-  Relies on `PyTensor `__ which provides:
    *  Computation optimization and dynamic C or JAX compilation
    *  NumPy broadcasting and advanced indexing
    *  Linear algebra operators
    *  Simple extensibility
-  Transparent support for missing value imputation

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

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

-  `API quickstart guide `__
-  The `PyMC tutorial `__
-  `PyMC examples `__ and the `API reference `__

Learn Bayesian statistics with a book together with PyMC
--------------------------------------------------------

-  `Bayesian Analysis with Python  `__ (third edition) by Osvaldo Martin: Great introductory book.
-  `Probabilistic Programming and Bayesian Methods for Hackers `__: Fantastic book with many applied code examples.
-  `PyMC port of the book "Doing Bayesian Data Analysis" by John Kruschke `__ as well as the `first edition `__.
-  `PyMC port of the book "Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath `__
-  `PyMC port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers `__: Focused on using Bayesian statistics in cognitive modeling.

Audio & Video
-------------

- Here is a `YouTube playlist `__ gathering several talks on PyMC.
- You can also find all the talks given at **PyMCon 2020** `here `__.
- The `"Learning Bayesian Statistics" podcast `__ helps you discover and stay up-to-date with the vast Bayesian community. Bonus: it's hosted by Alex Andorra, one of the PyMC core devs!

Installation
============

To install PyMC on your system, follow the instructions on the `installation guide `__.

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)
- |DOIzenodo| A DOI for all versions.
- DOIs for specific versions are shown on Zenodo and under `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

Contact
=======

We are using `discourse.pymc.io `__ as our main communication channel.

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 `__.

You can also follow us on these social media platforms for updates and other announcements:

- `LinkedIn @pymc `__
- `YouTube @PyMCDevelopers `__
- `Twitter @pymc_devs `__
- `Mastodon @pymc@bayes.club `__

To report an issue with PyMC please use the `issue tracker `__.

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

License
=======

`Apache License, Version
2.0 `__


Software using PyMC
===================

General purpose
---------------

- `Bambi `__: BAyesian Model-Building Interface (BAMBI) in Python.
- `calibr8 `__: A toolbox for constructing detailed observation models to be used as likelihoods in PyMC.
- `gumbi `__: A high-level interface for building GP models.
- `SunODE `__: Fast ODE solver, much faster than the one that comes with PyMC.
- `pymc-learn `__: Custom PyMC models built on top of pymc3_models/scikit-learn API

Domain specific
---------------

- `Exoplanet `__: a toolkit for modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series.
- `beat `__: Bayesian Earthquake Analysis Tool.
- `CausalPy `__: A package focussing on causal inference in quasi-experimental settings.

Please contact us if your software is not listed here.

Papers citing PyMC
==================

See Google Scholar `here `__ and `here `__ for a continuously updated list.

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

See the `GitHub contributor
page `__. Also read our `Code of Conduct `__ guidelines for a better contributing experience.

Support
=======

PyMC is a non-profit project under NumFOCUS umbrella. If you want to support PyMC financially, you can donate `here `__.

Professional Consulting Support
===============================

You can get professional consulting support from `PyMC Labs `__.

Sponsors
========

|NumFOCUS|

|PyMCLabs|

|Mistplay|

|ODSC|

Thanks to our contributors
==========================

|contributors|

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   :target: https://codecov.io/gh/pymc-devs/pymc
.. |Dockerhub| image:: https://img.shields.io/docker/automated/pymc/pymc.svg
   :target: https://hub.docker.com/r/pymc/pymc
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   :target: https://pymc-labs.io
.. |Mistplay| image:: https://github.com/pymc-devs/brand/blob/main/sponsors/sponsor_logos/sponsor_mistplay.png?raw=true
   :target: https://www.mistplay.com/
.. |ODSC| image:: https://github.com/pymc-devs/brand/blob/main/sponsors/sponsor_logos/odsc/sponsor_odsc.png?raw=true
   :target: https://odsc.com/california/?utm_source=pymc&utm_medium=referral
.. |contributors| image:: https://contrib.rocks/image?repo=pymc-devs/pymc
   :target: https://github.com/pymc-devs/pymc/graphs/contributors

Owner

Citation (CITATION.bib)

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

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