EXPLAINING THE GIBBS SAMPLER

被引：1493

作者：

CASELLA, G ^{[1
]}

GEORGE, EI ^{[1
]}

机构：

[1] UNIV TEXAS,DEPT MSIS,AUSTIN,TX 78712

来源：

AMERICAN STATISTICIAN | 1992年 / 46卷 / 03期

关键词：

DATA AUGMENTATION; MARKOV CHAINS; MONTE-CARLO METHODS; RESAMPLING TECHNIQUES;

D O I：

10.2307/2685208

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：

Computer-intensive algorithms, such as the Gibbs sampler, have become increasingly popular statistical tools, both in applied and theoretical work. The properties of such algorithms, however, may sometimes not be obvious. Here we give a simple explanation of how and why the Gibbs sampler works. We analytically establish its properties in a simple case and provide insight for more complicated cases. There are also a number of examples.

引用

页码：167 / 174

页数：8

共 38 条

[1]

ALBERT J, 1991, BAYESIAN ANAL BINARY

[2]

BESAG J, 1974, J ROY STAT SOC B MET, V36, P192

[3] SHRINKAGE ESTIMATION OF PRICE AND PROMOTIONAL ELASTICITIES - SEEMINGLY UNRELATED EQUATIONS [J].

BLATTBERG, RC ;

GEORGE, EI .

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1991, 86 (414) :304-315

[4]

CHURCHILL G, 1991, BU1138M CORN U BIOM

[5]

DELLAPORTAS P, 1990, BAYESIAN INFERENCE G

[6] MAXIMUM LIKELIHOOD FROM INCOMPLETE DATA VIA EM ALGORITHM [J].

DEMPSTER, AP ;

LAIRD, NM ;

RUBIN, DB .

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL, 1977, 39 (01) :1-38

[7]

Devroye L., 1986, NONUNIFORM RANDOM VA

[8]

DIEBOLT J, 1990, ESTIMATION FINITE MI

[9]

EDDY WF, 1990, ASYMPTOTIC DISTRIBUT

[10]

EFRON B, 1982, NATIONAL SCI F C BOA, V38

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