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European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty

ECSQARU 2001: Symbolic and Quantitative Approaches to Reasoning with Uncertainty pp 168–179Cite as

Importance Sampling in Bayesian Networks Using Antithetic Variables

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2143))

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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Author information

Authors and Affiliations

  1. Dpt. Statistics and Applied Mathematics, University of Almería, La Cañada de San Urbano s/n, 04120, Almería, Spain

    Antonio Salmerón

  2. Dpt. Computer Science and Artificial Intelligence, University of Granada, Avda. Andalucía 38, 18071, Granada, Spain

    Serafín Moral

Authors
  1. Antonio Salmerón
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  2. Serafín Moral
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Editor information

Editors and Affiliations

  1. Université Paul Sabatier, IRIT-CNRS, 118 route de Narbonne, 31062, Toulouse Cedex 4, France

    Salem Benferhat  & Philippe Besnard  & 

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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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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42464-2

  • Online ISBN: 978-3-540-44652-1

  • eBook Packages: Springer Book Archive

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