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:
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 “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:
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)
A DOI for all versions.
DOIs for specific versions are shown on Zenodo and under Releases
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:
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
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
5 maintainers
Metadata
5 maintainers
Release files for pymc 5.12.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pymc-5.12.0.tar.gz | 446.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pymc-5.12.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 920.2 kB
Release files / pymc-5.12.0.tar.gz
| Download URL | pymc-5.12.0.tar.gz |
|---|---|
| Size | 446.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
44e520986870db392f601d09b8b767527a7ea3f0b2cb59b0e7c34c3bc34f93da
|
|
BLAKE2b-256 checksum How to use checksums |
1fbe5abecce6bcf0287e0a2b440eba9024aa86564c940a22ea3b1614cc20eb3c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.7.17
|
Release files / pymc-5.12.0-py3-none-any.whl
| Download URL | pymc-5.12.0-py3-none-any.whl |
|---|---|
| Size | 473.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
81c214622e13625d4ba2f1ac6d24ba08b4271b9f0d180b441d188dc7637af1df
|
|
BLAKE2b-256 checksum How to use checksums |
dcc3cc84830c6c5c5ff266baa97d506aebdc20dcc4446feb4ea1233941311b6d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.7.17
|
