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