Top-Down Construction and Repetitive Structures Representation in Bayesian Networks

May 2000

Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2000)

Volume

Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2000)

All Papers

Bayesian networks for large and complex domains are difficult to construct and maintain. For example modifying a small network fragment in a repetitive structure might bevery time consuming. Top-down modelling may simplify the construction of large Bayesian networks, but methods (partly) supporting top-down modelling have only recently been introduced and tools do not exist. In this paper, we try to take a top-down approach to constructing Bayesian networks by using existing object oriented methods. We change these where they fail to support top-down modeling. This provides a new framework that allows top-down methodologies for the construction of Bayesian networks, provides an efficient class hierarchy and a compact way of specifying and representing temporal Bayesian networks. Furthermore, a conceptual simplification is achieved.