Learning With Bayesian Networks

Abstract

This chapter discusses a knowledge representation, called a Bayesian network, that allows one to learn uncertain relationships in a domain by combining expert domain knowledge and statistical data. A Bayesian network is a graphical representation of uncertain knowledge that most people find easy to construct directly from domain knowledge. In addition, the representation has formal probabilistic semantics, making it suitable for statistical manipulation. Over the past decade, the Bayesian network has become a popular representation for encoding uncertain expert knowledge in expert systems. More recently, researchers have developed methods for learning Bayesian networks from a combination of expert knowledge and data. The techniques that have been developed are new and still evolving, but they have been shown to be remarkably effective in some domains. Learning using Bayesian networks is similar to that using neural networks. The process employing Bayesian networks, however, has two important advantages: (1) one can easily encode expert knowledge in a Bayesian network, and use this knowledge to increase the efficiency and accuracy of learning; and (2) the nodes and arcs in learned Bayesian networks often correspond to recognizable distinctions and causal relationships.