This dissertation describes BNI (Belief Network Inductor), a tool that automatically induces a belief network from a database. The fundamental thrust of this research program has been to provide a theoretically sound method of inducing a model from data, and performing inference over that model. Along with a solid grounding in probability theory, BNI has proven to be a quick, practical method of inducing data models that are highly accurate. The results include a belief network that stores beta distributions in the conditional probability tables, coupled with theorems demonstrating how to maintain these distributions through inference; techniques for applying neural network and other learning techniques to the task of conditional probability table learning; and a decision theoretic sampling theory which addresses scalability issues by characterizing the size of the sample needed to produce high quality inferences.

The setting for this work is in database mining. Database mining is one of the fastest growing topics in Artificial Intelligence today, with industry providing at least as much impetus as research labs and universities. The general goal is to extract interesting quantities or relationships that are "hidden" in large corporate or scientific databases, with the potential benefits of a successful technology being enormous. For example, models can be built that characterize what types of customers will respond to what types of marketing schemes, retailers will be able to predict sales to help determine correct inventory levels and distribution schedules, and insurance companies will be able to predict expected claim costs and better classify who will buy what type of coverage.