Monte Carlo methods for Bayesian analysis of constrained parameter problems

被引：15
|
作者：
Chen, MH
Shao, QM
机构：
[1] Worcester Polytech Inst, Dept Math Sci, Worcester, MA 01609 USA
[2] Univ Oregon, Dept Math, Eugene, OR 97404 USA
来源：
BIOMETRIKA | 1998年 / 85卷 / 01期
基金：
美国国家科学基金会;
关键词：
Bayesian computation; Bayesian hierarchical model; Gibbs sampling; marginal posterior density estimation;
D O I：
10.1093/biomet/85.1.73
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Constraints on the parameters in a Bayesian hierarchical model typically make Bayesian computation and analysis complicated. Posterior densities that contain analytically intractable integrals as normalising constants depending on the hyperparameters often make implementation of Gibbs sampling or the Metropolis algorithms difficult. By, using reweighting mixtures (Geyer, 1995), we develop alternative simulation-based methods to determine properties of the desired Bayesian posterior distribution. Necessary theory and two illustrative examples are provided.
引用
收藏
页码：73 / 87
页数：15
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