Model choice using reversible jump Markov chain Monte Carlo

被引：71

作者：

Hastie, David I. ^{[1
]}

Green, Peter J. ^{[2
]}

机构：

[1] Imperial Coll London St Marys, Dept Epidemiol & Biostat, London W2 1PG, England

[2] Univ Bristol, Sch Math, Bristol BS8 1TW, Avon, England

来源：

STATISTICA NEERLANDICA | 2012年 / 66卷 / 03期

关键词：

across-model sampling; Bayes factors; Bayesian model determination; posterior model probabilities; transdimensional inference; variable dimension problems; BAYESIAN MODEL; PROPOSAL DISTRIBUTIONS; UNKNOWN NUMBER; MIXTURE-MODELS; MCMC; CONSTRUCTION; COMPUTATION; SIMULATION; COMPONENTS; SELECTION;

D O I：

10.1111/j.1467-9574.2012.00516.x

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：

We review the across-model simulation approach to computation for Bayesian model determination, based on the reversible jump Markov chain Monte Carlo method. Advantages, difficulties and variations of the methods are discussed. We also discuss some limitations of the ideal Bayesian view of the model determination problem, for which no computational methods can provide a cure.

引用

页码：309 / 338

页数：30

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