Self-regenerative Markov chain Monte Carlo with adaptation

被引：22

作者：

Sahu, SK ^{[1
]}

Zhigljavsky, AA

机构：

[1] Univ Southampton, Fac Math Studies, Southampton, Hants, England

[2] Univ Wales Coll Cardiff, Sch Math, Cardiff CF1 1XL, S Glam, Wales

来源：

BERNOULLI | 2003年 / 9卷 / 03期

关键词：

adaptive method; Bayesian inference; independence sampler; Metropolis-Hastings algorithm; regeneration;

D O I：

10.3150/bj/1065444811

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：

A new method of construction of Markov chains with a given stationary distribution is proposed. The method is based on constructing an auxiliary chain with some other stationary distribution and picking elements of this auxiliary chain a suitable number of times. The proposed method is easy to implement and analyse; it may be more efficient than other related Markov chain Monte Carlo techniques. The main attractive feature of the associated Markov chain is that it regenerates whenever it accepts a new proposed point. This makes the algorithm easy to adapt and tune for practical problems. A theoretical study and numerical comparisons with some other available Markov chain Monte Carlo techniques are presented.

引用

页码：395 / 422

页数：28

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