Reduction of Computational Complexity in Bayesian Networks through Removal of Weak Dependencies
This method reduces computational burden for users of Bayesian networks, but it is incremental as it builds on existing approximation techniques.
The paper tackles the problem of high computational complexity in Bayesian networks by removing weak dependencies, which dramatically reduces complexity by eliminating unnecessary fill-ins and moral links. Empirical evaluation on large real-world networks demonstrates its applicability.
The paper presents a method for reducing the computational complexity of Bayesian networks through identification and removal of weak dependencies (removal of links from the (moralized) independence graph). The removal of a small number of links may reduce the computational complexity dramatically, since several fill-ins and moral links may be rendered superfluous by the removal. The method is described in terms of impact on the independence graph, the junction tree, and the potential functions associated with these. An empirical evaluation of the method using large real-world networks demonstrates the applicability of the method. Further, the method, which has been implemented in Hugin, complements the approximation method suggested by Jensen & Andersen (1990).