ON THE CONVERGENCE RATES OF SOME ADAPTIVE MARKOV CHAIN MONTE CARLO ALGORITHMS

被引：1

作者：

Atchade, Yves ^{[1
]}

Wang, Yizao ^{[2
]}

机构：

[1] Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USA

[2] Univ Cincinnati, Dept Math Sci, Cincinnati, OH 45221 USA

来源：

JOURNAL OF APPLIED PROBABILITY | 2015年 / 52卷 / 03期

基金：

美国国家科学基金会;

关键词：

Adaptive Markov chain Monte Carlo; mixing time; total variation distance; importance resampling algorithm; equi-energy sampler; EFFICIENCY;

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：

In this paper we study the mixing time of certain adaptive Markov chain Monte Carlo (MCMC) algorithms. Under some regularity conditions, we show that the convergence rate of importance resampling MCMC algorithms, measured in terms of the total variation distance, is O (n(-1)). By means of an example, we establish that, in general, this algorithm does not converge at a faster rate. We also study the interacting tempering algorithm, a simplified version of the equi-energy sampler, and establish that its mixing time is of order O (n(-1/2)).

引用

页码：811 / 825

页数：15

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