Examples of Adaptive MCMC

被引：591

作者：

Roberts, Gareth O. ^{[1
]}

Rosenthal, Jeffrey S. ^{[2
]}

机构：

[1] Univ Lancaster, Fylde Coll, Dept Math & Stat, Lancaster LA1 4YF, England

[2] Univ Toronto, Dept Stat, Toronto, ON M5S 3G3, Canada

来源：

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS | 2009年 / 18卷 / 02期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Adaption; Convergence; Hierarchical models; Markov chain Monte Carlo; Metropolis algorithm; Metropolis-within-Gibbs; Nonconjugate priors; Non-Markovian; CHAIN MONTE-CARLO; MARKOV-CHAINS; CONVERGENCE; HASTINGS;

D O I：

10.1198/jcgs.2009.06134

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 investigate the use of adaptive MCMC algorithms to automatically tune the Markov chain parameters during a run. Examples include the Adaptive Metropolis (AM) multivariate algorithm of Haario, Saksman, and Tamminen (2001), Metropolis-within-Gibbs algorithms for nonconjugate hierarchical models, regionally adjusted Metropolis algorithms, and logarithmic scalings. Computer simulations indicate that the algorithms perform very well compared to nonadaptive algorithms, even in high dimension.

引用

页码：349 / 367

页数：19

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