An Empirical Analysis of Likelihood-Weighting Simulation on a Large, Multiply-Connected Belief Network

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 Markov blanket scoring and self-importance sampling significantly improve the convergence of the simulation on our model.