@misc{10481/77888,
year = {2004},
month = {9},
url = {https://hdl.handle.net/10481/77888},
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.
  2004 Elsevier Inc. All rights reserved.},
organization = {Spanish Ministry of Science and Technology, project Elvira II
(TIC2001-2973-C05-01 and 02)},
publisher = {Elsevier},
keywords = {Bayesian networks},
keywords = {Probability propagation},
keywords = {Approximate algorithms},
keywords = {Importance sampling},
keywords = {Probability trees},
keywords = {Inteligencia artificial},
keywords = {Artificial intelligence},
title = {Dynamic importance sampling in Bayesian networks based on probability trees},
doi = {10.1016/j.ijar.2004.05.005},
author = {Moral Callejón, Serafín},
}