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Stochastic simulation approaches perform probabilistic inference in Bayesian networks by estimating the probability of an event based on the frequency that that· event occurs in a set of simulation trials. This paper describes the evidence weighting mechanism, for augmenting the lo � ic sampling stochastic simulation algo rithm l5]. Evidence weighting modifies the logic sampling algorithm by weighting each simula tion trial by the likelihood of a network's evi dence given the sampled state node values for that trial. We also describe an enhancement to the basic algorithm which uses the eviden tial integration technique [2]. A comparison of the basic evidence weighting mechanism with the Markov blanket algorithm [8], the logic sampling algorithm, and the evidence integra tion algorithm is presented. The comparison is aided by analyzing the performance of the algorithms in a simple example network.

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