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