| Name | pymc JSON |
| Version |
5.15.0
JSON |
| download |
| home_page | http://github.com/pymc-devs/pymc |
| Summary | Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with PyTensor |
| upload_time | 2024-05-13 14:34:07 |
| maintainer | PyMC Developers |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | Apache License, Version 2.0 |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
.. image:: https://cdn.rawgit.com/pymc-devs/pymc/main/docs/logos/svg/PyMC_banner.svg
:height: 100px
:alt: PyMC logo
:align: center
|Build Status| |Coverage| |NumFOCUS_badge| |Binder| |Dockerhub| |DOIzenodo|
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 <https://docs.pymc.io/en/latest/learn/core_notebooks/pymc_overview.html>`__, or
one of `the many examples <https://www.pymc.io/projects/examples/en/latest/gallery.html>`__!
For questions on PyMC, head on over to our `PyMC Discourse <https://discourse.pymc.io/>`__ 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 <http://www.jmlr.org/papers/v15/hoffman14a.html>`__, allow complex models
with thousands of parameters with little specialized knowledge of
fitting algorithms.
- **Variational inference**: `ADVI <http://www.jmlr.org/papers/v18/16-107.html>`__
for fast approximate posterior estimation as well as mini-batch ADVI
for large data sets.
- Relies on `PyTensor <https://pytensor.readthedocs.io/en/latest/>`__ 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 <https://www.pymc.io/projects/examples/en/latest/introductory/api_quickstart.html>`__
- The `PyMC tutorial <https://docs.pymc.io/en/latest/learn/core_notebooks/pymc_overview.html>`__
- `PyMC examples <https://www.pymc.io/projects/examples/en/latest/gallery.html>`__ and the `API reference <https://docs.pymc.io/en/stable/api.html>`__
Learn Bayesian statistics with a book together with PyMC
--------------------------------------------------------
- `Bayesian Analysis with Python <http://bap.com.ar/>`__ (third edition) by Osvaldo Martin: Great introductory book.
- `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.
- `PyMC port of the book "Doing Bayesian Data Analysis" by John Kruschke <https://github.com/cluhmann/DBDA-python>`__ as well as the `first edition <https://github.com/aloctavodia/Doing_bayesian_data_analysis>`__.
- `PyMC 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>`__
- `PyMC 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.
Audio & Video
-------------
- Here is a `YouTube playlist <https://www.youtube.com/playlist?list=PL1Ma_1DBbE82OVW8Fz_6Ts1oOeyOAiovy>`__ gathering several talks on PyMC.
- You can also find all the talks given at **PyMCon 2020** `here <https://discourse.pymc.io/c/pymcon/2020talks/15>`__.
- The `"Learning Bayesian Statistics" podcast <https://www.learnbayesstats.com/>`__ 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 <https://www.pymc.io/projects/docs/en/latest/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)
- |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
Contact
=======
We are using `discourse.pymc.io <https://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 <https://discourse.pymc.io/c/questions>`__. You can also suggest feature in the `“Development” Category <https://discourse.pymc.io/c/development>`__.
You can also follow us on these social media platforms for updates and other announcements:
- `LinkedIn @pymc <https://www.linkedin.com/company/pymc/>`__
- `YouTube @PyMCDevelopers <https://www.youtube.com/c/PyMCDevelopers>`__
- `Twitter @pymc_devs <https://twitter.com/pymc_devs>`__
- `Mastodon @pymc@bayes.club <https://bayes.club/@pymc>`__
To report an issue with PyMC please use the `issue tracker <https://github.com/pymc-devs/pymc/issues>`__.
Finally, if you need to get in touch for non-technical information about the project, `send us an e-mail <info@pymc-devs.org>`__.
License
=======
`Apache License, Version
2.0 <https://github.com/pymc-devs/pymc/blob/main/LICENSE>`__
Software using PyMC
===================
General purpose
---------------
- `Bambi <https://github.com/bambinos/bambi>`__: BAyesian Model-Building Interface (BAMBI) in Python.
- `calibr8 <https://calibr8.readthedocs.io>`__: A toolbox for constructing detailed observation models to be used as likelihoods in PyMC.
- `gumbi <https://github.com/JohnGoertz/Gumbi>`__: A high-level interface for building GP models.
- `SunODE <https://github.com/aseyboldt/sunode>`__: Fast ODE solver, much faster than the one that comes with PyMC.
- `pymc-learn <https://github.com/pymc-learn/pymc-learn>`__: Custom PyMC models built on top of pymc3_models/scikit-learn API
Domain specific
---------------
- `Exoplanet <https://github.com/dfm/exoplanet>`__: a toolkit for modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series.
- `beat <https://github.com/hvasbath/beat>`__: Bayesian Earthquake Analysis Tool.
- `CausalPy <https://github.com/pymc-labs/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 <https://scholar.google.com/scholar?cites=6357998555684300962>`__ and `here <https://scholar.google.com/scholar?cites=6936955228135731011>`__ for a continuously updated list.
Contributors
============
See the `GitHub contributor
page <https://github.com/pymc-devs/pymc/graphs/contributors>`__. Also read our `Code of Conduct <https://github.com/pymc-devs/pymc/blob/main/CODE_OF_CONDUCT.md>`__ 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 <https://numfocus.salsalabs.org/donate-to-pymc3/index.html>`__.
Professional Consulting Support
===============================
You can get professional consulting support from `PyMC Labs <https://www.pymc-labs.io>`__.
Sponsors
========
|NumFOCUS|
|PyMCLabs|
|Mistplay|
|ODSC|
Thanks to our contributors
==========================
|contributors|
.. |Binder| image:: https://mybinder.org/badge_logo.svg
:target: https://mybinder.org/v2/gh/pymc-devs/pymc/main?filepath=%2Fdocs%2Fsource%2Fnotebooks
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:target: https://github.com/pymc-devs/pymc/actions
.. |Coverage| image:: https://codecov.io/gh/pymc-devs/pymc/branch/main/graph/badge.svg
: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
.. |NumFOCUS_badge| image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A
:target: http://www.numfocus.org/
.. |NumFOCUS| image:: https://github.com/pymc-devs/brand/blob/main/sponsors/sponsor_logos/sponsor_numfocus.png?raw=true
:target: http://www.numfocus.org/
.. |PyMCLabs| image:: https://github.com/pymc-devs/brand/blob/main/sponsors/sponsor_logos/sponsor_pymc_labs.png?raw=true
: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
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Its flexibility and extensibility make it applicable to a\nlarge suite of problems.\n\nCheck out the `PyMC overview <https://docs.pymc.io/en/latest/learn/core_notebooks/pymc_overview.html>`__, or\none of `the many examples <https://www.pymc.io/projects/examples/en/latest/gallery.html>`__!\nFor questions on PyMC, head on over to our `PyMC Discourse <https://discourse.pymc.io/>`__ forum.\n\nFeatures\n========\n\n- Intuitive model specification syntax, for example, ``x ~ N(0,1)``\n translates to ``x = Normal('x',0,1)``\n- **Powerful sampling algorithms**, such as the `No U-Turn\n Sampler <http://www.jmlr.org/papers/v15/hoffman14a.html>`__, allow complex models\n with thousands of parameters with little specialized knowledge of\n fitting algorithms.\n- **Variational inference**: `ADVI <http://www.jmlr.org/papers/v18/16-107.html>`__\n for fast approximate posterior estimation as well as mini-batch ADVI\n for large data sets.\n- Relies on `PyTensor <https://pytensor.readthedocs.io/en/latest/>`__ which provides:\n * Computation optimization and dynamic C or JAX compilation\n * NumPy broadcasting and advanced indexing\n * Linear algebra operators\n * Simple extensibility\n- Transparent support for missing value imputation\n\nGetting started\n===============\n\nIf you already know about Bayesian statistics:\n----------------------------------------------\n\n- `API quickstart guide <https://www.pymc.io/projects/examples/en/latest/introductory/api_quickstart.html>`__\n- The `PyMC tutorial <https://docs.pymc.io/en/latest/learn/core_notebooks/pymc_overview.html>`__\n- `PyMC examples <https://www.pymc.io/projects/examples/en/latest/gallery.html>`__ and the `API reference <https://docs.pymc.io/en/stable/api.html>`__\n\nLearn Bayesian statistics with a book together with PyMC\n--------------------------------------------------------\n\n- `Bayesian Analysis with Python <http://bap.com.ar/>`__ (third edition) by Osvaldo Martin: Great introductory book.\n- `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.\n- `PyMC port of the book \"Doing Bayesian Data Analysis\" by John Kruschke <https://github.com/cluhmann/DBDA-python>`__ as well as the `first edition <https://github.com/aloctavodia/Doing_bayesian_data_analysis>`__.\n- `PyMC 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>`__\n- `PyMC 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.\n\nAudio & Video\n-------------\n\n- Here is a `YouTube playlist <https://www.youtube.com/playlist?list=PL1Ma_1DBbE82OVW8Fz_6Ts1oOeyOAiovy>`__ gathering several talks on PyMC.\n- You can also find all the talks given at **PyMCon 2020** `here <https://discourse.pymc.io/c/pymcon/2020talks/15>`__.\n- The `\"Learning Bayesian Statistics\" podcast <https://www.learnbayesstats.com/>`__ 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!\n\nInstallation\n============\n\nTo install PyMC on your system, follow the instructions on the `installation guide <https://www.pymc.io/projects/docs/en/latest/installation.html>`__.\n\nCiting PyMC\n===========\nPlease choose from the following:\n\n- |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. 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You can also suggest feature in the `\u201cDevelopment\u201d Category <https://discourse.pymc.io/c/development>`__.\n\nYou can also follow us on these social media platforms for updates and other announcements:\n\n- `LinkedIn @pymc <https://www.linkedin.com/company/pymc/>`__\n- `YouTube @PyMCDevelopers <https://www.youtube.com/c/PyMCDevelopers>`__\n- `Twitter @pymc_devs <https://twitter.com/pymc_devs>`__\n- `Mastodon @pymc@bayes.club <https://bayes.club/@pymc>`__\n\nTo report an issue with PyMC please use the `issue tracker <https://github.com/pymc-devs/pymc/issues>`__.\n\nFinally, if you need to get in touch for non-technical information about the project, `send us an e-mail <info@pymc-devs.org>`__.\n\nLicense\n=======\n\n`Apache License, Version\n2.0 <https://github.com/pymc-devs/pymc/blob/main/LICENSE>`__\n\n\nSoftware using PyMC\n===================\n\nGeneral purpose\n---------------\n\n- `Bambi <https://github.com/bambinos/bambi>`__: BAyesian Model-Building Interface (BAMBI) in Python.\n- `calibr8 <https://calibr8.readthedocs.io>`__: A toolbox for constructing detailed observation models to be used as likelihoods in PyMC.\n- `gumbi <https://github.com/JohnGoertz/Gumbi>`__: A high-level interface for building GP models.\n- `SunODE <https://github.com/aseyboldt/sunode>`__: Fast ODE solver, much faster than the one that comes with PyMC.\n- `pymc-learn <https://github.com/pymc-learn/pymc-learn>`__: Custom PyMC models built on top of pymc3_models/scikit-learn API\n\nDomain specific\n---------------\n\n- `Exoplanet <https://github.com/dfm/exoplanet>`__: a toolkit for modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series.\n- `beat <https://github.com/hvasbath/beat>`__: Bayesian Earthquake Analysis Tool.\n- `CausalPy <https://github.com/pymc-labs/CausalPy>`__: A package focussing on causal inference in quasi-experimental settings.\n\nPlease contact us if your software is not listed here.\n\nPapers citing PyMC\n==================\n\nSee Google Scholar `here <https://scholar.google.com/scholar?cites=6357998555684300962>`__ and `here <https://scholar.google.com/scholar?cites=6936955228135731011>`__ for a continuously updated list.\n\nContributors\n============\n\nSee the `GitHub contributor\npage <https://github.com/pymc-devs/pymc/graphs/contributors>`__. 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