Dynamic importance sampling in Bayesian networks based on probability trees Moral Callejón, Serafín Bayesian networks Probability propagation Approximate algorithms Importance sampling Probability trees Inteligencia artificial Artificial intelligence 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. 2004 Elsevier Inc. All rights reserved. 2022-11-10T12:05:00Z 2022-11-10T12:05:00Z 2004-09-17 conference output Serafín Moral, Antonio Salmerón, Dynamic importance sampling in Bayesian networks based on probability trees, International Journal of Approximate Reasoning, Volume 38, Issue 3, 2005, Pages 245-261, ISSN 0888-613X, [https://doi.org/10.1016/j.ijar.2004.05.005] https://hdl.handle.net/10481/77888 10.1016/j.ijar.2004.05.005 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier