PyAgrum is a Python library for probabilistic graphical models. It provides comprehensive tools for working with Bayesian networks, Markov networks, and related models, enabling users to create, edit, and perform inference efficiently.

If you want to cite aGrUM/pyAgrum, please use : aGrUM/pyAgrum : a Toolbox to Build Models and Algorithms for Probabilistic Graphical Models in Python, International Conference on Probabilistic Graphical Models, Sep 2020, Skørping, Denmark.

You can install PyAgrum by following the instructions in our installation guide, which covers various platforms and environments.

The official PyAgrum documentation is available on our documentation section, featuring tutorials, API references, and practical examples. You can also access the latest documentation on Read the Docs.

Yes, PyAgrum is open source and distributed under a dual license (LGPLv3 and MIT). The source code is hosted on GitLab, and contributions from the community are welcome.

We welcome contributions! You can contribute by reporting issues, suggesting features, or submitting pull requests. Please refer to our contribution guidelines for more details.

For support, you can join our LinkedIn group or reach out via our GitLab issues page. We also have a Discord server for real-time discussions.

Yes, PyAgrum's dual licensing allows for both open-source and commercial use. Please refer to the license page for more information.