|
Abstract : |
I would like to thank Kate Fowler, Bob Krieger and Meliani Suwandi for many helpful discussions and for implementing the generation algorithm, and Susan McRoy for helpful comments on an earlier draft. This work was partially supported by NSF grant #IRI9207262. We present a method for dynamically generating Bayesian networks from knowledge bases consisting of first-order probability logic sentences. We present a subset of probability logic sufficient for representing the class of Bayesian networks with discrete-valued nodes. We impose constraints on the form of the sentences that guarantee that the knowledge base contains all the probabilistic information necessary to generate a network. We define the concept of d-separation for knowledge bases and prove that a knowledge base with independence conditions defined by d-separation is a complete specification of a probability distribution. We present a network generation algorithm that, given an inference problem in the form of a query Q and a set of evidence E, generates a network to compute P (QjE). We prove the algorithm to be correct. 1, |
