Python Environment for Bayesian Learning
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docs Small changes to facilitate building RPM packages for Fedora Feb 1, 2010
src/pebl Fixing bug in discretizer: Issue #40 Mar 16, 2010
INSTALL.txt Adding INSTALL.txt as a symlink to docs/src/install.rst. Jun 23, 2008
LICENSE.txt * Changed license to MIT May 7, 2008 Small changes to facilitate building RPM packages for Fedora Feb 1, 2010 Copying over README from google code. Nov 16, 2011 Initial commit to google code svn. Aug 28, 2007
pebl-logo.png Transitioning from google code to github. Nov 16, 2011
setup.cfg Fixing a bug in the tutorial and updating version for upcoming inclus… Jan 18, 2010


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.


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.