Abstract. This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabifistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probahilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.Keywords. probabilistic networks, Bayesian belief networks, machine learning, induction
I n t r o d u c t i o nIn this paper, we present a Bayesian method for constructing a probabilistic network from a database of records, which we call cases. Once constructed, such a network can provide insight into probabilistic dependencies that exist among the variables in the database. One application is the automated discovery of dependency relationships. The computer program searches for a probabilistic-network structure that has a high posterior probability given the database, and outputs the structure and its probability. A related task is computer-assisted hypothesis testing: The user enters a hypothetical structure of the dependency relationships among a set of variables, and the program calculates the probability of the structure given a database of cases on the variables.We can also construct a network and use it for computer-based diagnosis. For example, suppose we have a database in which a case contains data about the behavior of some system (i.e., findings). Suppose further that a case contains data about whether this particular behavior follows from proper system operation, or alternatively, is caused by one of several possible faults. Assume that the database contains many such cases from previous episodes of proper and faulty behavior. The method that we present in this paper can be used to construct from the database a probabilistic network that captures the probabilistic dependencies among findings and faults. Such a network then can be applied to classify future cases of system behavior by assigning a posterior probability to each of the possible faults and to the event "proper system operation." In this paper, we also shall discuss diagnostic inference that is based on combining the inferences of multiple alternative networks.