This research investigates Bayesian methods for constructing probabilistic networks from databases. Its main focus is on constructing Bayesian belief networks. Primary goals are to (1) develop methods for calculating the probability of a Bayesian belief-network structure given a database of cases, (2) identify the most probable belief-network structure given a database of cases, and (3) perform probabilistic inference by taking a weighted average over the inferences of multiple belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. Methods will be explored for intergrateing prior knowledge with data, handling missing data, and discovering hidden (latent) variables. Of particular concern is the development of computationally efficient algorithms. The methods developed will be empirically evaluated using databases from several domains. //
