BNT structure learning package: Documentation and experiments

Bayesian networks are a formalism for probabilistic reasoning that have grown in-creasingly popular for tasks such as classification in data-mining. In some situations, the structure of the Bayesian network can be given by an expert. If not, retrieving it automatically from a database of cases is a NP-hard problem; notably because of the complexity of the search space. In the last decade, numerous methods have been introduced to learn the network’s structure automatically, by simplifying the search space or by using an heuristic in the search space. Most methods deal with completely observed data, but some can deal with incomplete data. The Bayes Net Toolbox for Matlab, introduced by Murphy (2004), offers functions for both using and learning Bayesian Networks. But this toolbox is not ’state of the art ’ as regards structural learning methods. This is why we propose the SLP package