Coupling and ergodicity of adaptive Markov chain Monte Carlo algorithms

被引：237

作者：

Roberts, Gareth O. ^{[1
]}

Rosenthal, Jeffrey S.

机构：

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

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

来源：

JOURNAL OF APPLIED PROBABILITY | 2007年 / 44卷 / 02期

关键词：

Markov chains; computational methods;

D O I：

10.1239/jap/1183667414

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 basic ergodicity properties of adaptive Markov chain Monte Carlo algorithms under minimal assumptions, using coupling constructions. We prove convergence in distribution and a weak law of large numbers. We also give counterexamples to demonstrate that the assumptions we make are not redundant.

引用

页码：458 / 475

页数：18

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