A Snakemake workflow to run and benchmark structure learning (a.k.a. causal discovery) algorithms for probabilistic graphical models.
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Updated
Jul 24, 2024 - Python
A Snakemake workflow to run and benchmark structure learning (a.k.a. causal discovery) algorithms for probabilistic graphical models.
Python 3.7 version of David Barber's MATLAB BRMLtoolbox
Bayesian structure learning and classification in decomposable graphical models.
Graph: Representation, Learning, and Inference Methods
This is a collection of algorithms and models written in Python for probabilistic programming. The main focus of the package is on Bayesian reasoning by using Bayesian networks, Markov networks, and their mixing.
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