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Bayesian Modeling and Probabilistic Programming in Python
https://github.com/pymc-devs/pymc

bayesian-inference mcmc probabilistic-programming pytensor python statistical-analysis variational-inference

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Bayesian Modeling and Probabilistic Programming in Python

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README 

        
.. 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



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 `__

-  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 `__

- `X @pymc_devs `__

- `Mastodon @[email protected] `__



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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