Top-down Speci cation of Bayesian Networks and Compact Representation of Repetitive Structures in Bayesiean Networks

Bayesian networks are not easy to design and maintain. It is a time consuming process to update a Bayesian network even though only a small set of nodes with many occurrences has to be changed. In this paper, we describe a solution to these diiculties by taking an object oriented approach to constructing Bayesian networks by merging fragments of Bayesian networks. Our approach consists of a new framework based on the framework presented in Koller and Pfeeer, 1997]. Our framework allows top-down methodolo-gies for the design of Bayesian networks, provides an eecient class hierarchy and a compact way of specifying and representing temporal Bayesian networks. Furthermore a conceptual simpliication is achieved. It will be possible to design, maintain and use each fragment as a unit. Updating such a unit updates each occurence of this unit in the whole Bayesian network.