Uncertainty in Artificial Intelligence
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Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
Robert Fung, Kuo-Chu Chang
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.
Pages: 112-117
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PDF Link: /papers/89/p112-fung.pdf
AUTHOR = "Robert Fung and Kuo-Chu Chang",
TITLE = "Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks",
BOOKTITLE = "Proceedings of the Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-89)",
ADDRESS = "Corvallis, Oregon",
YEAR = "1989",
PAGES = "112--117"

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