Using Causal Information and Local Measures to Learn Bayesian Networks

In previous work we developed a method of learning Bayesian Network models from raw data. This method relies on the well known minimal description length (MDL) principle. The MDL principle is particularly well suited to this task as it allows us to tradeo , in a principled way, the accuracy of the learned network against its practical usefulness. In this paper we present some new results that have arisen from our work. In particular, we present a new local way of computing the description length. This allows us to make signi cant improvements in our search algorithm. In addition, we modify our algorithm so that it can take into account partial domain information that might be provided by a domain expert. The local computation of description length also opens the door for local re nement of an existent network. The feasibility of our approach is demonstrated by experiments involving networks of a practical size. Appears in Proceedings of Uncertainty in Arti cial Intelligence 1993 Pages...

• Type: Conference
• Year: 1993
• Where: UAI
• Authors: Wai Lam, Fahiem Bacchus
• Keywords: Bayesian Network Models, Description Length, Minimal Description Length, UAI 1993, UAI 2008