Efficient Markov chain Monte Carlo with incomplete multinomial data

被引：0

作者：

Kwang Woo Ahn

Kung-Sik Chan

机构：

[1] Medical College of Wisconsin,Division of Biostatistics

[2] The University of Iowa,Department of Statistics and Actuarial Science

来源：

Statistics and Computing | 2010年 / 20卷

关键词：

Blocking; Gibbs sampler; Dirichlet distribution; Epidemiology;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

We propose a block Gibbs sampling scheme for incomplete multinomial data. We show that the new approach facilitates maximal blocking, thereby reducing serial dependency and speeding up the convergence of the Gibbs sampler. We compare the efficiency of the new method with the standard, non-block Gibbs sampler via a number of numerical examples.

引用

页码：447 / 456

页数：9

共 16 条

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