The Annals of Applied Probability

A sequential Monte Carlo approach to computing tail probabilities in stochastic models

Hock Peng Chan and Tze Leung Lai

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Sequential Monte Carlo methods which involve sequential importance sampling and resampling are shown to provide a versatile approach to computing probabilities of rare events. By making use of martingale representations of the sequential Monte Carlo estimators, we show how resampling weights can be chosen to yield logarithmically efficient Monte Carlo estimates of large deviation probabilities for multidimensional Markov random walks.

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Ann. Appl. Probab., Volume 21, Number 6 (2011), 2315-2342.

First available in Project Euclid: 23 November 2011

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Zentralblatt MATH identifier

Primary: 60F10: Large deviations 65C05: Monte Carlo methods
Secondary: 60J22: Computational methods in Markov chains [See also 65C40] 60K35: Interacting random processes; statistical mechanics type models; percolation theory [See also 82B43, 82C43]

Exceedance probabilities large deviations logarithmic efficiency sequential importance sampling and resampling


Chan, Hock Peng; Lai, Tze Leung. A sequential Monte Carlo approach to computing tail probabilities in stochastic models. Ann. Appl. Probab. 21 (2011), no. 6, 2315--2342. doi:10.1214/10-AAP758.

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