pymc_bayesian

https://github.com/kehanli-1123/pymc_bayesian

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
--------------------------------------------------------

-  `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 `second edition `__: 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 `__
-  `PyMC port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers `__: Focused on using Bayesian statistics in cognitive modeling.
-  `Bayesian Analysis with Python  `__ (second edition) by Osvaldo Martin: Great introductory book. (`code `__ and errata).

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| *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 `_

.. |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 `__ 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 `__ 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|

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Owner

Citation (CITATION.bib)

@article{Salvatier2016,
  doi = {10.7717/peerj-cs.55},
  url = {https://doi.org/10.7717/peerj-cs.55},
  year  = {2016},
  month = {apr},
  publisher = {{PeerJ}},
  volume = {2},
  pages = {e55},
  author = {John Salvatier and Thomas V. Wiecki and Christopher Fonnesbeck},
  title = {Probabilistic programming in Python using {PyMC}3},
  journal = {{PeerJ} Computer Science}
}

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