Weighing and Integrating Evidence for Stochastic Simulation in Bayesian
  Networks
  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.
