  Stochastic simulation approaches perform probabilistic inference in Bayesian
networks by estimating the probability of an event based on the frequency that
the event occurs in a set of simulation trials. This paper describes the
evidence weighting mechanism, for augmenting the logic sampling stochastic
simulation algorithm [Henrion, 1986]. Evidence weighting modifies the logic
sampling algorithm by weighting each simulation trial by the likelihood of a
network's evidence given the sampled state node values for that trial. We also
describe an enhancement to the basic algorithm which uses the evidential
integration technique [Chin and Cooper, 1987]. A comparison of the basic
evidence weighting mechanism with the Markov blanket algorithm [Pearl, 1987],
the logic sampling algorithm, and the evidence integration algorithm is
presented. The comparison is aided by analyzing the performance of the
algorithms in a simple example network.
