An empirical analysis of likelihood-weighting simulation on a large, multiply-connected belief network
Shwe MA, Cooper GF. An empirical analysis of likelihood-weighting simulation on a large, multiply-connected belief network. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence (1990) 498–508.
We analyzed the convergence properties of likelihoodweighting 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.