Lazy Propagation and Independence of Causal Influence

Authors:
Anders L. Madsen;Bruce D'Ambrosio
Affiliations:
-;-
Venue:
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Year:
1999

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Abstract

The efficiency of algorithms for probabilistic inference in Bayesian networks can be improved by exploiting independence of causal influence In this paper we propose a method to exploit independence of causal influence based on on-line construction of decomposition trees. The efficiency of inference is improved by exploiting independence relations induced by evidence during decomposition tree construction. We also show how a factorized representation of independence of causal influence can be derived from a local expression language. The factorized representation is shown to fit ideally with the lazy propagation framework. Empirical results indicate that considerable efficiency improvements can be expected if either the decomposition trees are constructed on-line or the factorized representation is used.