MODELING EXPERTS' OPINIONS BY USING BAYESIAN MIXTURE MODELS FOR A SUBCLASS OF THE EXPONENTIAL FAMILY

被引：0

作者：

Rufo, M. J. ^{[1
]}

Martin, J. ^{[2
]}

Perez, C. J. ^{[1
]}

机构：

[1] Univ Extremadura, Dept Math, Caceres, Spain

[2] Univ Extremadura, Dept Math, Badajoz, Spain

来源：

PAKISTAN JOURNAL OF STATISTICS | 2009年 / 25卷 / 04期

关键词：

Bayesian analysis; beliefs' aggregation; conjugate prior distributions; exponential families; mixture of distributions; APPROXIMATING PRIORS; DISTRIBUTIONS;

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：

This paper provides a Bayesian unified framework to analyze mixture models of one-parameter distributions from a subclass of the exponential family. Firstly, the problem of choosing a suitable prior distribution on the component distributions is addressed in a context where the number of components is known, In order to do this, several experts' opinions are assumed as conjugate prior distributions. Then, a mixture model of these prior distributions is considered to represent a consensus among experts. The weights are fixed and they must be calculated. Next, the label-switching problem is presented and a general algorithm to solve this difficulty for the considered family is provided. An illustrative example shows that the proposal techniques are easily applied in practice.

引用

页码：595 / 613

页数：19

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