Discovering Structure in Continuous Variables Using Bayesian Networks
Reimar Hofmann, Volker Tresp · 1995
We study Bayesian networks for continuous variables using nonlinear conditional density estimators. We demonstrate that useful structures can be extracted from a data set in a self-organized way and we present sampling techniques for belief update based on Markov blanket conditional density models. 1 Introduction One of the strongest types of information that can be learned about an unknown process is the discovery of dependencies and ---even more important--- of independencies. A superior example is medical epidemiology where the goal is to find the causes of a disease and exclude factors which are irrelevant. Whereas complete independence between two variables in a domain might be rare in reality (which would mean that the joint probability density of variables A and B can be factored: p(A; B) = p(A)p(B)), conditional independence is more common and is often a result from true or apparent causality: consider the case that A is the cause of B and B is the cause of C, then p(CjA; B)...