A sequential Monte Carlo approach to computing tail probabilities in stochastic models
Probability
2012-02-22 v1
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.
Cite
@article{arxiv.1202.4582,
title = {A sequential Monte Carlo approach to computing tail probabilities in stochastic models},
author = {Hock Peng Chan and Tze Leung Lai},
journal= {arXiv preprint arXiv:1202.4582},
year = {2012}
}
Comments
Published in at http://dx.doi.org/10.1214/10-AAP758 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)