April 23, 1999
School of Information Technology and Engineering
University of Ottawa
Learning Bayes Belief Networks: Test Data Generation
In this talk we present the problem of learning in belief networks. Our goal is to build a probabilistic network from the distribution of the data which adequately represents the data. It is assumed that no information about the probability is available. We consider representations using Chow's dependence trees and Polytrees (Singly Connected Networks) as structures for inferring causal knowledge if the training samples are given. To test the validity of the theoretical algorithms that we have developed we propose a method for generating a sample data obeying an underlying structure which can be any Directed Acyclic Graph. The results of this process will be used to test the learning algorithms.