SIMPLE CONDITIONS FOR THE CONVERGENCE OF THE GIBBS SAMPLER AND METROPOLIS-HASTINGS ALGORITHMS

被引：178

作者：

ROBERTS, GO

SMITH, AFM

机构：

[1] UNIV CAMBRIDGE,CAMBRIDGE,ENGLAND

[2] UNIV LONDON IMPERIAL COLL SCI TECHNOL & MED,LONDON SW7 2AZ,ENGLAND

来源：

STOCHASTIC PROCESSES AND THEIR APPLICATIONS | 1994年 / 49卷 / 02期

关键词：

MARKOV CHAIN MONTE-CARLO; GIBBS SAMPLER; METROPOLIS-HASTINGS ALGORITHM; STATISTICAL COMPUTATION; ERGODICITY; LOWER SEMICONTINUITY;

D O I：

10.1016/0304-4149(94)90134-1

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：

Markov chain Monte Carlo (MCMC) simulation methods are being used increasingly in statistical computation to explore and estimate features of likelihood surfaces and Bayesian posterior distributions. This paper presents simple conditions which ensure the convergence of two widely used versions of MCMC, the Gibbs sampler and Metropolis-Hastings algorithms.

引用

页码：207 / 216

页数：10

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