An Empirical Analysis of Likelihood-Weighting Simulation on a Large, Multiply-Connected Belief Network
Michael Shwe, Gregory Cooper
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, demonstrating that the Markov blanket scoring and self-importance sampling significantly improve the convergence of the simulation on our model.
PDF Link: /papers/90/p498-shwe.pdf
AUTHOR = "Michael Shwe
and Gregory Cooper",
TITLE = "An Empirical Analysis of Likelihood-Weighting Simulation on a Large, Multiply-Connected Belief Network",
BOOKTITLE = "Proceedings of the Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-90)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1990",
PAGES = "498--508"