Metadata-Version: 2.1
Name: pymc
Version: 5.4.1
Summary: Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with PyTensor
Home-page: http://github.com/pymc-devs/pymc
Maintainer: PyMC Developers
Maintainer-email: pymc.devs@gmail.com
License: Apache License, Version 2.0
Description: .. 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/howto/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
        --------------------------------------------------------
        
        -  `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/aloctavodia/Doing_bayesian_data_analysis>`__ as well as the `second edition <https://github.com/JWarmenhoven/DBDA-python>`__: Principled introduction to 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.
        -  `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).
        
        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| *Probabilistic programming in Python using PyMC3*, Salvatier J., Wiecki T.V., Fonnesbeck C. (2016)
        - |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.55-blue
             :target: https://doi.org/10.7717/peerj-cs.55
        .. |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 <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/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|
        
        .. |Binder| image:: https://mybinder.org/badge_logo.svg
           :target: https://mybinder.org/v2/gh/pymc-devs/pymc/main?filepath=%2Fdocs%2Fsource%2Fnotebooks
        .. |Build Status| image:: https://github.com/pymc-devs/pymc/workflows/pytest/badge.svg
           :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/
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           :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/
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/x-rst
