Dynamic importance sampling in Bayesian networks based on probability trees

Authors:
Serafín Moral;Antonio Salmerón
Affiliations:
Department Computer Science and Artificial Intelligence, University of Granada, Avda. Andalucía 38, 18071 Granada, Spain;Department Statistics and Applied Mathematics, University of Almería, La Cañada de San Urbano s/n, 04120 Almería, Spain
Venue:
International Journal of Approximate Reasoning
Year:
2005

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Abstract

In this paper we introduce a new dynamic importance sampling propagation algorithm for Bayesian networks. Importance sampling is based on using an auxiliary sampling distribution from which a set of configurations of the variables in the network is drawn, and 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 achieved with a low 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.