Sequential Monte Carlo without likelihoods

被引：517

作者：

Sisson, S. A. ^{[1
]}

Fan, Y.

Tanaka, Mark M.

机构：

[1] Univ New S Wales, Sch Math & Stat, Sydney, NSW 2052, Australia

[2] Univ New S Wales, Sch Biotechnol & Biomol Sci, Sydney, NSW 2052, Australia

来源：

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA | 2007年 / 104卷 / 06期

关键词：

approximate Bayesian computation; Bayesian inference; importance sampling; intractable likelihoods; tuberculosis;

D O I：

10.1073/pnas.0607208104

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributions in the presence of analytically or computationally intractable likelihood functions. Despite representing a substantial methodological advance, existing methods based on rejection sampling or Markov chain Monte Carlo can be highly inefficient and accordingly require far more iterations than may be practical to implement. Here we propose a sequential Monte Carlo sampler that convincingly overcomes these inefficiencies. We demonstrate its implementation through an epidemiological study of the transmission rate of tuberculosis.

引用

页码：1760 / 1765

页数：6

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