ON MONTE CARLO ESTIMATION OF LARGE DEVIATIONS PROBABILITIES
成果类型：
Article
署名作者：
Sadowsky, John S.
署名单位：
Arizona State University; Arizona State University-Tempe
刊物名称：
ANNALS OF APPLIED PROBABILITY
ISSN/ISSBN：
1050-5164
发表日期：
1996
页码：
399-422
关键词：
摘要：
Importance sampling is a Monte Carlo technique where random data are sampled from an alternative sampling distribution and an unbiased estimator is obtained by likelihood ratio weighting. Here we consider estimation of large deviations probabilities via importance sampling. Previous works have shown, for certain special cases, that exponentially twisted distributions possess a strong asymptotic optimality property as a sampling distribution. The results of this paper unify and generalize the previous special case results. The analysis is presented in an abstract setting, so the results are quite general and directly applicable to a number of large deviations problems. Our main motivation, however, is to attack sample path problems. To illustrate the application to this class of problems, we consider Mogulskii type sample path problems in some detail.