ON THE CONTAINMENT CONDITION FOR ADAPTIVE MARKOV CHAIN MONTE CARLO ALGORITHMS

被引：0
|
作者：
Bai, Yan [1 ]
Roberts, Gareth O. [2 ]
Rosenthal, Jeffrey S. [1 ]
机构：
[1] Univ Toronto, Dept Stat, Toronto, ON M5S 3G3, Canada
[2] Univ Warwick, Dept Stat, Coventry CV4 7AL, W Midlands, England
来源：
ADVANCES AND APPLICATIONS IN STATISTICS | 2011年 / 21卷 / 01期
基金：
加拿大自然科学与工程研究理事会;
关键词：
Markov chain Monte Carlo; adaptive algorithms; ergodicity; diminishing adaptation; Containment;
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：
This paper considers ergodicity properties of certain adaptive Markov chain Monte Carlo (MCMC) algorithms for multidimensional target distributions. It was previously shown in [23] that Diminishing Adaptation and Containment imply ergodicity of adaptive MCMC. We derive various sufficient conditions to ensure Containment.
引用
收藏
页码：1 / 54
页数：54
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