BAYESIAN COMPUTATION VIA THE GIBBS SAMPLER AND RELATED MARKOV-CHAIN MONTE-CARLO METHODS

被引：27

作者：

SMITH, AFM ^{[1
]}

ROBERTS, GO ^{[1
]}

机构：

[1] UNIV CAMBRIDGE,CAMBRIDGE,ENGLAND

来源：

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL | 1993年 / 55卷 / 01期

关键词：

BAYESIAN STATISTICS; CENSORED DATA; CONSTRAINED PARAMETER MODELS; GENERALIZED LINEAR MODELS; GIBBS SAMPLER; HASTINGS ALGORITHM; HIERARCHICAL MODELS; MARKOV CHAIN MONTE CARLO METHODS; MISSING DATA; TIME SERIES MODELS;

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：

The use of the Gibbs sampler for Bayesian computation is reviewed and illustrated in the context of some canonical examples. Other Markov chain Monte Carlo simulation methods are also briefly described, and comments are made on the advantages of sample-based approaches for Bayesian inference summaries.

引用

页码：3 / 23

页数：21

共 52 条

[1] ON RATES OF CONVERGENCE OF STOCHASTIC RELAXATION FOR GAUSSIAN AND NON-GAUSSIAN DISTRIBUTIONS [J].