Large deviations for weighted empirical measures arising in importance sampling

被引：3

作者：

Hult, Henrik ^{[1
]}

Nyquist, Pierre ^{[1
]}

机构：

[1] KTH, Dept Math, S-10044 Stockholm, Sweden

来源：

STOCHASTIC PROCESSES AND THEIR APPLICATIONS | 2016年 / 126卷 / 01期

关键词：

Large deviations; Empirical measures; Importance sampling; Monte Carlo;

D O I：

10.1016/j.spa.2015.08.002

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this paper the efficiency of an importance sampling algorithm is studied by means of large deviations for the associated weighted empirical measure. The main result, stated as a Laplace principle for these weighted empirical measures, can be viewed as an extension of Sanov's theorem. The main theorem is used to quantify the performance of an importance sampling algorithm over a collection of subsets of a given target set as well as quantile estimates. The analysis yields an estimate of the sample size needed to reach a desired precision and of the reduction in cost compared to standard Monte Carlo. (C) 2015 Published by Elsevier B.V.

引用

页码：138 / 170

页数：33

共 13 条

[1]

[Anonymous], 2007, Stochastic Modelling and Applied Probability

[2] Analysis of an interacting particle method for rare event estimation [J].