AND/OR importance sampling

Vibhav Gogate; Rina Dechter

AND/OR importance sampling

Vibhav Gogate, Rina Dechter

Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:212-219, 2008.

Abstract

The paper introduces AND/OR importance sampling for probabilistic graphical models. In contrast to importance sampling, AND/OR importance sampling caches samples in the AND/OR space and then extracts a new sample mean from the stored samples. We prove that AND/OR importance sampling may have lower variance than importance sampling; thereby providing a theoretical justification for preferring it over importance sampling. Our empirical evaluation demonstrates that AND/OR importance sampling is far more accurate than importance sampling in many cases.

Cite this Paper

BibTeX

@InProceedings{pmlr-vR6-gogate08a,
  title = 	 {AND/OR importance sampling},
  author =       {Gogate, Vibhav and Dechter, Rina},
  booktitle = 	 {Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence},
  pages = 	 {212--219},
  year = 	 {2008},
  editor = 	 {McAllester, David A. and Myllymäki, Petri},
  volume = 	 {R6},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {09--12 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://raw.githubusercontent.com/mlresearch/r6/main/assets/gogate08a/gogate08a.pdf},
  url = 	 {https://proceedings.mlr.press/r6/gogate08a.html},
  abstract = 	 {The paper introduces AND/OR importance sampling for probabilistic graphical models. In contrast to importance sampling, AND/OR importance sampling caches samples in the AND/OR space and then extracts a new sample mean from the stored samples. We prove that AND/OR importance sampling may have lower variance than importance sampling; thereby providing a theoretical justification for preferring it over importance sampling. Our empirical evaluation demonstrates that AND/OR importance sampling is far more accurate than importance sampling in many cases.},
  note =         {Reissued by PMLR on 09 October 2024.}
}

Endnote

%0 Conference Paper
%T AND/OR importance sampling
%A Vibhav Gogate
%A Rina Dechter
%B Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence
%C Proceedings of Machine Learning Research
%D 2008
%E David A. McAllester
%E Petri Myllymäki	
%F pmlr-vR6-gogate08a
%I PMLR
%P 212--219
%U https://proceedings.mlr.press/r6/gogate08a.html
%V R6
%X The paper introduces AND/OR importance sampling for probabilistic graphical models. In contrast to importance sampling, AND/OR importance sampling caches samples in the AND/OR space and then extracts a new sample mean from the stored samples. We prove that AND/OR importance sampling may have lower variance than importance sampling; thereby providing a theoretical justification for preferring it over importance sampling. Our empirical evaluation demonstrates that AND/OR importance sampling is far more accurate than importance sampling in many cases.
%Z Reissued by PMLR on 09 October 2024.

APA

Gogate, V. & Dechter, R.. (2008). AND/OR importance sampling. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R6:212-219 Available from https://proceedings.mlr.press/r6/gogate08a.html. Reissued by PMLR on 09 October 2024.

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