Learning with Bayesian networks and probability trees to approximate a joint distribution

Most of learning algorithms with Bayesian networks try to minimize the number of structural errors (missing, added or inverted links in the learned graph with respect to the true one). In this paper we assume that the objective of the learning task is to approximate the joint probability distribution of the data. For this aim, some experiments have shown that learning with probability trees to represent the conditional probability distributions of each node given its parents provides better results that learning with probability tables. When approximating a joint distribution structure and parameter learning can not be seen as separated tasks and we have to evaluate the performance of combinations of procedures for inducing both structure and parameters. We carry out an experimental evaluation of several combined strategies based on trees and tables using a greedy hill climbing algorithm and compare the results with a restricted search procedure (the Max-Min hill climbing algorithm).