Dynamic importance sampling computation in Bayesian networks

Abstract

This paper proposes a new version of importance sampling propagation algorithms: dynamic importance sampling. Importance sampling is based on using an auxiliary sampling distribution. The performance of the algorithm depends on the variance of the weights associated with the simulated configurations. The basic idea of dynamic importance sampling is to use the simulation of a configuration to modify the sampling distribution in order to improve its quality and so reducing the variance of the future weights. The paper shows that this can be done with little computational effort. The experiments carried out show that the final results can be very good even in the case that the initial sampling distribution is far away from the optimum.