|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Pymc||7,603||143||95||14 hours ago||36||March 15, 2022||198||other||Python|
|Bayesian Modeling in Python|
|Bayesian_changepoint_detection||547||1||7 months ago||1||August 12, 2019||10||mit||Jupyter Notebook|
|Methods to get the probability of a changepoint in a time series.|
|Bayestestr||522||11||23||5 days ago||23||September 18, 2022||40||gpl-3.0||R|
|:ghost: Utilities for analyzing Bayesian models and posterior distributions|
|Bayesian Statistics||288||4 months ago||1||cc-by-sa-4.0||TeX|
|This repository holds slides and code for a full Bayesian statistics graduate course.|
|Optimized Naive Bayesian classifier for NodeJS|
|Probflow||108||2 years ago||12||December 28, 2020||16||mit||Python|
|A Python package for building Bayesian models with TensorFlow or PyTorch|
|Drbayes||76||7 years ago||September 20, 2022||4||lgpl-3.0||Racket|
|Multibayes||74||1||1||5 years ago||May 30, 2021||2||Go|
|Multiclass Naive Bayesian Classification|
|Pylearn||72||8 years ago||1||mit||Python|
|Bayesian machine learning in Python|
|Lampe||62||19 days ago||2||mit||Python|
|Likelihood-free AMortized Posterior Estimation with PyTorch|
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.
x ~ N(0,1)translates to
x = Normal('x',0,1)
To install PyMC on your system, follow the instructions on the installation guide.
Please choose from the following:
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.
Please contact us if your software is not listed here.
See Google Scholar for a continuously updated list.
See the GitHub contributor page. Also read our Code of Conduct guidelines for a better contributing experience.
PyMC is a non-profit project under NumFOCUS umbrella. If you want to support PyMC financially, you can donate here.
You can get professional consulting support from PyMC Labs.