Attention: In order to compile these programs the table package must also be retrieved.

Description

Programs to learn a graphical model (Bayesian network or possibilistic network) from a dataset of sample cases, to generate a random dataset from a Bayesian network, to evaluate learned networks w.r.t. a test dataset and a reference network, and to measure the strengths of conditional dependences.

A brief description of how to apply these programs can be found in the file ines/ex/readme in the source package. The scripts djc_prob, djc_poss, and djc_local in the directory ines/djc may also be helpful.

The theory underlying this program is described in detail in the book:

• Graphical Models - Methods for Data Analysis and Mining
  Christian Borgelt and Rudolf Kruse.
  J. Wiley and Sons, Chichester, United Kingdom 2002, ISBN 0-470-84337-3