|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Pymc||7,785||143||105||7 hours ago||36||March 15, 2022||216||other||Python|
|Bayesian Modeling in Python|
|Bda_py_demos||851||2 years ago||gpl-3.0||Jupyter Notebook|
|Bayesian Data Analysis demos for Python|
|Bayespy||666||4 months ago||5||March 20, 2021||68||mit||Python|
|Bayesian Python: Bayesian inference tools for Python|
|Dbda Python||543||2 years ago||3||mit||Jupyter Notebook|
|Doing Bayesian Data Analysis, 2nd Edition (Kruschke, 2015): Python/PyMC3 code|
|Bda_r_demos||530||12 days ago||2||bsd-3-clause||R|
|Bayesian Data Analysis demos for R|
|Bayesplot||376||6 months ago||60||gpl-3.0||R|
|bayesplot R package for plotting Bayesian models|
|Scipy2014_tutorial||281||4 years ago||1||Jupyter Notebook|
|Tutorial: Bayesian Statistical Analysis in Python|
|Mamba.jl||228||13||3 years ago||November 11, 2019||43||other||Julia|
|Markov chain Monte Carlo (MCMC) for Bayesian analysis in julia|
|Beast2||217||8 days ago||160||lgpl-2.1||Java|
|Bayesian Evolutionary Analysis by Sampling Trees|
|Pygpgo||214||1||2 years ago||5||July 27, 2021||6||mit||Python|
|Bayesian optimization for Python|
BayesPy provides tools for Bayesian inference with Python. The user constructs a model as a Bayesian network, observes data and runs posterior inference. The goal is to provide a tool which is efficient, flexible and extendable enough for expert use but also accessible for more casual users.
Currently, only variational Bayesian inference for conjugate-exponential family (variational message passing) has been implemented. Future work includes variational approximations for other types of distributions and possibly other approximate inference methods such as expectation propagation, Laplace approximations, Markov chain Monte Carlo (MCMC) and other methods. Contributions are welcome.
Copyright (C) 2011-2017 Jaakko Luttinen and other contributors (see below)
BayesPy including the documentation is licensed under the MIT License. See LICENSE file for a text of the license or visit http://opensource.org/licenses/MIT.
|Author||Jaakko Luttinen [email protected]|
|Mailing list||[email protected]|
|Branch||Test status||Test coverage||Documentation|
Bayes Blocks (http://research.ics.aalto.fi/bayes/software/) is a C++/Python implementation of the variational building block framework. The framework allows easy learning of a wide variety of models using variational Bayesian learning. It is available as free software under the GNU General Public License.
Infer.NET (http://research.microsoft.com/infernet/) is a .NET framework for machine learning. It provides message-passing algorithms and statistical routines for performing Bayesian inference. It is partly closed source and licensed for non-commercial use only.
The list of contributors:
Each file or the git log can be used for more detailed information.