A modified simulation scheme for inference in Bayesian networks

AbstractWe introduce an approximation method for uncertainty propagation based on a modification of the stratified simulation. The method uses a deterministic or perfect sample and calculates the number of times simulated instantiations are selected, avoiding the repetition of identical instantiations which occurs in the standard stratified simulation method. A theoretical analysis is presented to evaluate the performance of the method in comparison with the stratified simulation scheme. The analysis gives a technique to select the required step for the estimation of probabilities with a given error. Some experimental studies compare the proposed with other simulation methods and show a large performance improvement in computation time as well as in simulation errors