Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
This work addresses the challenge of efficient stochastic simulation for researchers and practitioners in probabilistic AI, but it is incremental as it builds upon and modifies existing algorithms.
The paper tackles the problem of probabilistic inference in Bayesian networks by introducing an evidence weighting mechanism to augment the logic sampling stochastic simulation algorithm, resulting in a comparison showing performance improvements over existing methods like the Markov blanket and evidence integration algorithms in a simple example network.
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.