Learning Bayesian Networks: Search Methods and Experimental Results
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:112-128, 1995.
We discuss Bayesian approaches for learning Bayesian networks from data. First, we review a metric for computing the relative posterior probability of a network structure given data developed by Heckerman et al. (1994 a, b, c). We see that the metric has a property useful for inferring causation from data. Next, we describe search methods for identifying network structures with high posterior probabilities. We describe polynomial algorithms for finding the highestscoring network structures in the special case where every node has at most $k=1$ parent. We show that the general case $(k>1)$ is NP-hard, and review heuristic search algorithms for this general case. Finally, we describe a methodology for evaluating learning algorithms, and use this methodology to evaluate various scoring metrics and search procedures.