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
收藏
页码：458 / 475
页数：18
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