]> 2012-02-11T07:51:17-05:00 A Bayesian Method for the Induction of Probabilistic Networks from Data This paper presents a Bayesian method for constructing probabilistic networks from a database of cases. In particular, we focus on constructing Bayesian belief networks. Applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic 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. techreport January,1991 A Bayesian Method for the Induction of Probabilistic Networks from Data KSL-91-02 1991 A Bayesian Method for the Induction of Probabilistic Networks from Data 1991 Year Title NO Process note Number January Month Ksl tr id Knowledge Systems, AI Laboratory Institution Has year Has where published Has title Has publishing details Has identifier Has author Bibtype Gregory F. Cooper and Edward Herskovits Author Stanford, CA, USA Address Abstract KSL Technical Report Publication Technical Report