Please use this identifier to cite or link to this item: https://doi.org/10.1214/10-AAP758
Title: A Sequential Monte Carlo approach to computing tail probabilities in stochastic models
Authors: Chan, H.P. 
Lai, T.L.
Keywords: Exceedance probabilities
Large deviations
Logarithmic efficiency
Sequential importance sampling and resampling
Issue Date: Dec-2011
Citation: Chan, H.P., Lai, T.L. (2011-12). A Sequential Monte Carlo approach to computing tail probabilities in stochastic models. Annals of Applied Probability 21 (6) : 2315-2342. ScholarBank@NUS Repository. https://doi.org/10.1214/10-AAP758
Abstract: 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. © Institute of Mathematical Statistics, 2011.
Source Title: Annals of Applied Probability
URI: http://scholarbank.nus.edu.sg/handle/10635/104969
ISSN: 10505164
DOI: 10.1214/10-AAP758
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