Software
Bayesian network structure learning
using an hybrid approach
Here below is a free software written in R by Maxime Gasse (Ph.D. student)
that implements an efficient and scalable hybrid algorithm for Bayesian network structure
learning, called Hybrid HPC (H2PC). It first reconstructs the skeleton
of a Bayesian network and then performs a Bayesian-scoring greedy
hill-climbing search to orient the edges. The software implements several procedures discussed in our recent ECML-PKDD 2012 article. It requires several routines implemented in the bnlearn
package in R developped by Marco Scutari. The source code of H2PC as well as all data sets used for the empirical
tests are freely available.
The code is distributed in the hope that it will be useful to researchers and bayesian netwok practitionners,
but without any warranty; without even the implied warranty of
merchantability or fitness for a particular purpose.
This program is free software; you can redistribute it and/or modify it under the terms of the
GNU General Public License as published by the Free Software Foundation.
Download H2PC latest version (July 1st 2012)
Download the data sets used in the ECML paper
