Bayesian analysis for finite Markov chains

被引：0

作者：

Bhat, BR ^{[1
]}

Badade, MN ^{[1
]}

机构：

[1] Univ Botswana, Dept Stat, Gaborone, Botswana

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 1999年 / 81卷 / 02期

关键词：

statistical inference; Markov chains; Bayes tests; posterior probabilities (P-values); odds ratio; Bayes factor; central credibility set; highest density region; umbrella pattern prior;

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：

Bayesian approach to inference for Markov chains (MC) has many advantages over classical approach. This paper discusses how tests for one-sided and two-sided hypotheses involving two or more parameters of finite Markov chains can be carried out. The posterior probabilities (P-values), Bayes factors, highest density regions (HDR) and central credible sets (CCS) and other measures are calculated for uniform and umbrella pattern prior distributions and for several functions of the two parameters in a two-state Markov chain. A numerical example is also worked out. (C) 1999 Elsevier Science B.V. All rights reserved.

引用

页码：335 / 348

页数：14

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