Abstract
In this paper we introduce an improvement over importance sampling propagation algorithms in Bayesian networks. The difference with respect to importance sampling is that during the simulation, configurations are obtained using antithetic variables (variables with negative correlation), achieving a reduction of the variance of the estimation. The performance of the new algorithm is tested by means of some experiments carried out over four large real-world networks.
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© 2001 Springer-Verlag Berlin Heidelberg
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Salmerón, A., Moral, S. (2001). Importance Sampling in Bayesian Networks Using Antithetic Variables. In: Benferhat, S., Besnard, P. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2001. Lecture Notes in Computer Science(), vol 2143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44652-4_16
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DOI: https://doi.org/10.1007/3-540-44652-4_16
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