Logical Compilation of Bayesian Networks with Discrete Variables

Authors:
Michael Wachter;Rolf Haenni
Affiliations:
University of Bern, Switzerland;University of Bern, Switzerland and Bern University of Applied Sciences, Switzerland
Venue:
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2007

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Abstract

This paper presents a new direction in the area of compiling Bayesian networks. The principal idea is to encode the network by logical sentences and to compile the resulting encoding into an appropriate form. From there, all possible queries are answerable in linear time relative to the size of the logical form. Therefore, our approach is a potential solution for real-time applications of probabilistic inference with limited computational resources. The underlying idea is similar to both the differential and the weighted model counting approach to inference in Bayesian networks, but at the core of the proposed encoding we avoid the transformation from discrete to binary variables. This alternative encoding enables a more natural solution.