Posterior consistency of Dirichlet mixtures in density estimation
成果类型：
Article
署名作者：
Ghosal, S; Ghosh, JK; Ramamoorthi, RV
署名单位：
Vrije Universiteit Amsterdam; Indian Statistical Institute; Indian Statistical Institute Kolkata; Michigan State University; Purdue University System; Purdue University
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
发表日期：
1999
页码：
143-158
关键词：
摘要：
A Dirichlet mixture of normal densities is a useful choice for a prior distribution on densities in the problem of Bayesian density estimation. In the recent years, efficient Markov chain Monte Carlo method for the computation of the posterior distribution has been developed. The method has been applied to data arising from different fields of interest. The important issue of consistency was however left open. In this paper, we settle this issue in affirmative.