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The paper presents a method for reducing the computational complexity ofBayesian networks through identification and removal of weak dependencies(removal of links from the (moralized) independence graph). The removal of asmall number of links may reduce the computational complexity dramatically,since several fill-ins and moral links may be rendered superfluous by theremoval. The method is described in terms of impact on the independence graph,the junction tree, and the potential functions associated with these. Anempirical evaluation of the method using large real-world networks demonstratesthe applicability of the method. Further, the method, which has beenimplemented in Hugin, complements the approximation method suggested by Jensen& Andersen (1990).

Reduction of Computational Complexity in Bayesian Networks through Removal of Weak Dependencies