On rates of convergence for posterior distributions in infinite-dimensional models

• 成果类型: Article
• 署名作者: Walker, Stephen G.; Lijoi, Antonio; Prunster, Igor
• 署名单位: University of Kent; University of Pavia; University of Turin
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053606000001361
• 发表日期: 2007
• 页码: 738-746
• 关键词: bayesian density-estimation; bernstein polynomials; Consistency; mixtures

摘要

This paper introduces a new approach to the study of rates of convergence for posterior distributions. It is a natural extension of a recent approach to the study of Bayesian consistency. In particular, we improve on current rates of convergence for models including the mixture of Dirichlet process model and the random Bernstein polynomial model.