Reference: Shwe, M. & Cooper, G. An Empirical Analysis of Likelihood-Weighting Simulation on a Large, Multiply-Connected Belief Network. 1991.
Abstract: We analyzed the convergence properties of likelihood-weighting algorithms on a two-level, multiply connected, belief-network representation of the QMR knowledge base of internal medicine. Specifically, on two difficult diagnostic cases, we examined the effects of Markov blanket scoring, importance sampling, and self-importance sampling, demonstrating that the simulation on our model requires the Markov blanket scoring and self-importance sampling to converge well.