agrumery/aGrUM
This repository is a mirror. If you want to raise an issue or contact us, we encourage you to do it on Gitlab (https://gitlab.com/agrumery/aGrUM).
This project helps data scientists, researchers, and decision-makers build, analyze, and use complex probabilistic models like Bayesian networks. You can input your data and domain knowledge to create graphical models, and then use them to understand relationships, predict outcomes, or make informed decisions. It provides tools for learning these models from data, performing probabilistic calculations, and even explaining the reasoning behind the models.
Use this if you need to model uncertain relationships in your data, make probabilistic predictions, or derive decisions from complex interacting factors, especially when leveraging Bayesian networks or causal analysis.
Not ideal if you are looking for a simple, out-of-the-box machine learning model for standard classification or regression tasks without the need for explicit probabilistic graphical modeling or causal inference.
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Last pushed
Feb 20, 2026
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