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

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

2012-02-21

arxiv.org/abs/1202.4582v1

Annals of Applied Probability 2011, Vol. 21, No. 6, 2315-2342

Mathematics

Probability

Published in at http://dx.doi.org/10.1214/10-AAP758 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Inst

Scientific paper

10.1214/10-AAP758

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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