A Flexible Software System for Learning Bayesian Networks from data

Abstract

Bayesian networks, also referred to as belief networks, originates from the artificial intelligence field where they were used to reason about uncertain knowledge. They differ from other knowledge representation schemes as they constitute a model of the environment rather than a model of the reasoning process. Among the Bayesian networks' main assets is that they offer a sound methodology for combining (a priori) information a domain expert may have with information available in databases. In this report a software system for learning Bayesian networks from data is described.