CONVERGENCE OF CONDITIONAL METROPOLIS-HASTINGS SAMPLERS

被引：0

作者：

Jones, Galin L. ^{[1
]}

Roberts, Gareth O. ^{[2
]}

Rosenthal, Jeffrey S. ^{[3
]}

机构：

[1] Univ Minnesota, Sch Stat, Minneapolis, MN 55455 USA

[2] Univ Warwick, Dept Stat, Coventry CV4 7AL, W Midlands, England

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

来源：

ADVANCES IN APPLIED PROBABILITY | 2014年 / 46卷 / 02期

基金：

美国国家科学基金会; 加拿大自然科学与工程研究理事会;

关键词：

Markov chain Monte Carlo algorithm; independence sampler; Gibbs sampler; geometric ergodicity; convergence rate; CHAIN MONTE-CARLO; EXPLORING POSTERIOR DISTRIBUTIONS; RANDOM EFFECTS MODEL; GIBBS SAMPLER; MARKOV-CHAINS; GEOMETRIC ERGODICITY; ALGORITHMS; RATES; ESTIMATORS; INFERENCE;

D O I：

暂无

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 consider Markov chain Monte Carlo algorithms which combine Gibbs updates with Metropolis Hastings updates, resulting in a conditional Metropolis Hastings sampler (CMH sampler). We develop conditions under which the CMH sampler will be geometrically or uniformly ergodic. We illustrate our results by analysing a CMH sampler used for drawing Bayesian inferences about the entire sample path of a diffusion process, based only upon discrete observations.

引用

页码：422 / 445

页数：24

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