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 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.