Examples of Adaptive MCMC

被引:573
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
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.
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页码:349 / 367
页数:19
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