On population-based simulation for static inference

被引：113

作者：

Jasra, Ajay

Stephens, David A.

Holmes, Christopher C.

机构：

[1] Univ London Imperial Coll Sci Technol & Med, Dept Math, London SW7 2AZ, England

[2] McGill Univ, Dept Math & Stat, Montreal, PQ H3A 2K6, Canada

[3] Univ Oxford, Dept Stat, Oxford OX1 3TG, England

来源：

STATISTICS AND COMPUTING | 2007年 / 17卷 / 03期

基金：

英国医学研究理事会;

关键词：

Markov chain Monte Carlo; sequential Monte Carlo; Bayesian mixture models; adaptive methods;

D O I：

10.1007/s11222-007-9028-9

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper we present a review of population-based simulation for static inference problems. Such methods can be described as generating a collection of random variables {X-n} (n=1,...,N) in parallel in order to simulate from some target density pi (or potentially sequence of target densities). Population-based simulation is important as many challenging sampling problems in applied statistics cannot be dealt with successfully by conventional Markov chain Monte Carlo (MCMC) methods. We summarize population-based MCMC (Geyer, Computing Science and Statistics: The 23rd Symposium on the Interface, pp. 156-163, 1991; Liang and Wong, J. Am. Stat. Assoc. 96, 653-666, 2001) and sequential Monte Carlo samplers (SMC) (Del Moral, Doucet and Jasra, J. Roy. Stat. Soc. Ser. B 68, 411-436, 2006a), providing a comparison of the approaches. We give numerical examples from Bayesian mixture modelling (Richardson and Green, J. Roy. Stat. Soc. Ser. B 59, 731-792, 1997).

引用

页码：263 / 279

页数：17

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[11] Stopping-time resampling for sequential Monte Carlo methods [J].