Inference in Hybrid Bayesian Networks with Deterministic Variables

Authors:
Prakash P. Shenoy;James C. West
Affiliations:
University of Kansas School of Business, Lawrence, USA KS 66045-7585;University of Kansas School of Business, Lawrence, USA KS 66045-7585
Venue:
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2009

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Abstract

The main goal of this paper is to describe an architecture for solving large general hybrid Bayesian networks (BNs) with deterministic variables. In the presence of deterministic variables, we have to deal with non-existence of joint densities. We represent deterministic conditional distributions using Dirac delta functions. Using the properties of Dirac delta functions, we can deal with a large class of deterministic functions. The architecture we develop is an extension of the Shenoy-Shafer architecture for discrete BNs. We illustrate the architecture with some small illustrative examples.