Learning the Structure of a Bayesian Network: An Application of Information Geometry and the Minimum Description Length Principle

• Main Author: Lauria, Eitel J M
• Format: Conference Proceeding
• Language: English
• ISSN: 0094-243X
• DOI: 10.1063/1.2149807

The present paper addresses the issue of learning the underlying structure of a discrete binary Bayesian network, expressed as a directed acyclic graph, which includes the specification of the conditional independence assumptions among the attributes of the model; and given the model, the conditional probability distributions that quantify those dependencies. The approach followed in this work heuristically searches the space of network structures using a scoring function based on the Minimum Description Length Principle, that takes into account the volume of the model manifold. Empirical results on synthetic datasets are presented, that analyse the underlying properties and relative effectiveness of this information geometric score, when varying the size and complexity of a Bayesian network.