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

页码：1 / 54

页数：54

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