pymc 5.28.0
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with PyTensor
License: Apache License, Version 2.0
Requires Python: >=3.11

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    :height: 100px
    :alt: PyMC logo
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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 <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


Linear Regression Example
==========================


Plant growth can be influenced by multiple factors, and understanding these relationships is crucial for optimizing agricultural practices.

Imagine we conduct an experiment to predict the growth of a plant based on different environmental variables.

.. code-block:: python

   import pymc as pm

   # Taking draws from a normal distribution
   seed = 42
   x_dist = pm.Normal.dist(shape=(100, 3))
   x_data = pm.draw(x_dist, random_seed=seed)

   # Independent Variables:
   # Sunlight Hours: Number of hours the plant is exposed to sunlight daily.
   # Water Amount: Daily water amount given to the plant (in milliliters).
   # Soil Nitrogen Content: Percentage of nitrogen content in the soil.


   # Dependent Variable:
   # Plant Growth (y): Measured as the increase in plant height (in centimeters) over a certain period.


   # Define coordinate values for all dimensions of the data
   coords={
    "trial": range(100),
    "features": ["sunlight hours", "water amount", "soil nitrogen"],
   }

   # Define generative model
   with pm.Model(coords=coords) as generative_model:
      x = pm.Data("x", x_data, dims=["trial", "features"])

      # Model parameters
      betas = pm.Normal("betas", dims="features")
      sigma = pm.HalfNormal("sigma")

      # Linear model
      mu = x @ betas

      # Likelihood
      # Assuming we measure deviation of each plant from baseline
      plant_growth = pm.Normal("plant growth", mu, sigma, dims="trial")


   # Generating data from model by fixing parameters
   fixed_parameters = {
    "betas": [5, 20, 2],
    "sigma": 0.5,
   }
   with pm.do(generative_model, fixed_parameters) as synthetic_model:
      idata = pm.sample_prior_predictive(random_seed=seed) # Sample from prior predictive distribution.
      synthetic_y = idata.prior["plant growth"].sel(draw=0, chain=0)


   # Infer parameters conditioned on observed data
   with pm.observe(generative_model, {"plant growth": synthetic_y}) as inference_model:
      idata = pm.sample(random_seed=seed)

      summary = pm.stats.summary(idata, var_names=["betas", "sigma"])
      print(summary)


From the summary, we can see that the mean of the inferred parameters are very close to the fixed parameters

=====================  ======  =====  ========  =========  ===========  =========  ==========  ==========  =======
Params                  mean     sd    hdi_3%    hdi_97%    mcse_mean    mcse_sd    ess_bulk    ess_tail    r_hat
=====================  ======  =====  ========  =========  ===========  =========  ==========  ==========  =======
betas[sunlight hours]   4.972  0.054     4.866      5.066        0.001      0.001        3003        1257        1
betas[water amount]    19.963  0.051    19.872     20.062        0.001      0.001        3112        1658        1
betas[soil nitrogen]    1.994  0.055     1.899      2.107        0.001      0.001        3221        1559        1
sigma                   0.511  0.037     0.438      0.575        0.001      0            2945        1522        1
=====================  ======  =====  ========  =========  ===========  =========  ==========  ==========  =======

.. code-block:: python

   # Simulate new data conditioned on inferred parameters
   new_x_data = pm.draw(
      pm.Normal.dist(shape=(3, 3)),
      random_seed=seed,
   )
   new_coords = coords | {"trial": [0, 1, 2]}

   with inference_model:
      pm.set_data({"x": new_x_data}, coords=new_coords)
      pm.sample_posterior_predictive(
         idata,
         predictions=True,
         extend_inferencedata=True,
         random_seed=seed,
      )

   pm.stats.summary(idata.predictions, kind="stats")

The new data conditioned on inferred parameters would look like:

================ ======== ======= ======== =========
Output            mean     sd      hdi_3%   hdi_97%
================ ======== ======= ======== =========
plant growth[0]   14.229   0.515   13.325   15.272
plant growth[1]   24.418   0.511   23.428   25.326
plant growth[2]   -6.747   0.511   -7.740   -5.797
================ ======== ======= ======== =========

.. code-block:: python

   # Simulate new data, under a scenario where the first beta is zero
   with pm.do(
    inference_model,
    {inference_model["betas"]: inference_model["betas"] * [0, 1, 1]},
   ) as plant_growth_model:
      new_predictions = pm.sample_posterior_predictive(
         idata,
         predictions=True,
         random_seed=seed,
      )

   pm.stats.summary(new_predictions, kind="stats")

The new data, under the above scenario would look like:

================ ======== ======= ======== =========
Output            mean     sd      hdi_3%   hdi_97%
================ ======== ======= ======== =========
plant growth[0]   12.149   0.515   11.193   13.135
plant growth[1]   29.809   0.508   28.832   30.717
plant growth[2]   -0.131   0.507   -1.121    0.791
================ ======== ======= ======== =========

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.

See also the section on books using PyMC on `our website <https://www.pymc.io/projects/docs/en/stable/learn/books.html>`__.

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)

  - BibTex version

    .. code:: bibtex

       @article{pymc2023,
         title = {{PyMC}: A Modern and Comprehensive Probabilistic Programming Framework in {P}ython},
         author = {Oriol Abril-Pla and Virgile Andreani and Colin Carroll and Larry Dong and Christopher J. Fonnesbeck and Maxim Kochurov and Ravin Kumar and Junpeng Lao and Christian C. Luhmann and Osvaldo A. Martin and Michael Osthege and Ricardo Vieira and Thomas Wiecki and Robert Zinkov },
         journal = {{PeerJ} Computer Science},
         volume = {9},
         number = {e1516},
         doi = {10.7717/peerj-cs.1516},
         year = {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
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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>`__.
Requests for non-technical information about the project are also welcome on Discourse,
we also use Discourse internally for general announcements or governance related processes.

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>`__
- `X @pymc_devs <https://x.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>`__.

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 focusing on causal inference in quasi-experimental settings.
- `PyMC-Marketing <https://github.com/pymc-labs/pymc-marketing>`__: Bayesian marketing toolbox for marketing mix modeling, customer lifetime value, and more.

See also the `ecosystem page <https://www.pymc.io/about/ecosystem.html>`__ on our website. 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
============

The `GitHub contributor page <https://github.com/pymc-devs/pymc/graphs/contributors>`__ shows the people who have added content to this repo
which includes a large portion of contributors to the PyMC project but not all of them. Other
contributors have added content to other repos of the ``pymc-devs`` GitHub organization or have contributed
through other project spaces outside of GitHub like `our Discourse forum <https://discourse.pymc.io/>`__.

If you are interested in contributing yourself, read our `Code of Conduct <https://github.com/pymc-devs/pymc/blob/main/CODE_OF_CONDUCT.md>`__
and `Contributing guide <https://www.pymc.io/projects/docs/en/latest/contributing/index.html>`__.

Support
=======

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

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

You can get professional consulting support from `PyMC Labs <https://www.pymc-labs.io>`__.

Sponsors
========

|NumFOCUS|

|PyMCLabs|

|OpenWoundResearch|

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

|contributors|

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