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May 1999 Adaptive importance sampling on discrete Markov chains
Keith Baggerly, Dennis Cox, Craig Kollman, Rick Picard
Ann. Appl. Probab. 9(2): 391-412 (May 1999). DOI: 10.1214/aoap/1029962748

Abstract

In modeling particle transport through a medium, the path of a particle behaves as a transient Markov chain. We are interested in characteristics of the particle's movement conditional on its starting state, which take the form of a "score" accumulated with each transition. Importance sampling is an essential variance reduction technique in this setting, and we provide an adaptive (iteratively updated) importance sampling algorithm that converges exponentially to the solution. Examples illustrating this phenomenon are provided.

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Keith Baggerly. Dennis Cox. Craig Kollman. Rick Picard. "Adaptive importance sampling on discrete Markov chains." Ann. Appl. Probab. 9 (2) 391 - 412, May 1999. https://doi.org/10.1214/aoap/1029962748

Information

Published: May 1999
First available in Project Euclid: 21 August 2002

zbMATH: 0939.65009
MathSciNet: MR1687335
Digital Object Identifier: 10.1214/aoap/1029962748

Subjects:
Primary: 65C05

Keywords: Adaptive procedures , exponential convergence , Monte Carlo , particle transport , zero-variance solution

Rights: Copyright © 1999 Institute of Mathematical Statistics

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Vol.9 • No. 2 • May 1999
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