Mieczysław Alojzy Kłopotek

Structure and Reasoning in Bayesian Networks

941

Abstract

Bayesian networks have many practical applications due to their capability to represent joint probability distribution in many variables in a compact way. Though there exist many algorithms for learning Bayesian networks from data, they are not satisfactory because the learned networks usually are not suitable for reasoning. So far only a restricted class of very simple Bayesian networks: trees and poly-trees are directly applicable in reasoning.

This paper defines and explores a new class of networks: the Structured Bayesian Networks. Two methods of reasoning are outlined for this type of networks. Possible methods of learning from data are indicated. Similarity to hierarchical networks is pointed at.

Keywords : Bayesian networks, reasoning, structured networks, hierarchical networks.