Python Environment for Bayesian Learning

Last update: Jul 14, 2022

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Overview

Pebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. Pebl includes the following features:

Can learn with observational and interventional data
Handles missing values and hidden variables using exact and heuristic methods
Provides several learning algorithms; makes creating new ones simple
Has facilities for transparent parallel execution using several cluster and cloud resources
Calculates edge marginals and consensus networks
Presents results in a variety of formats

Pebl has been developed at the Systems Biology Lab at the University of Michigan and in available under a permissive MIT-style license.

Documentation

Pebl is published in the Journal of Machine Learning Research. Please cite the paper if you use Pebl for your work. Abstract and PDF.

Documentation and tutorial in doc/src. Online version available at the Python Package Index.

Owner

Abhik Shah

GitHub Repository

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