Uncertainty in Artificial Intelligence
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AND/OR Importance Sampling
Vibhav Gogate, Rina Dechter
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.
Keywords: null
Pages: 212-219
PS Link:
PDF Link: /papers/08/p212-gogate.pdf
AUTHOR = "Vibhav Gogate and Rina Dechter",
TITLE = "AND/OR Importance Sampling",
BOOKTITLE = "Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2008",
PAGES = "212--219"

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