A method for combining inference across related nonparametric Bayesian models

• 成果类型: Article
• 署名作者: Müller, P; Quintana, F; Rosner, G
• 署名单位: University of Texas System; UTMD Anderson Cancer Center; Pontificia Universidad Catolica de Chile; University of Texas System
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2004.05564.x
• 发表日期: 2004
• 页码: 735-749
• 关键词: polya tree distributions; dirichlet process prior; sampling methods; mixture priors; count data

摘要

We consider the problem of combining inference in related nonparametric Bayes models. Analogous to parametric hierarchical models, the hierarchical extension formalizes borrowing strength across the related submodels. In the nonparametric context, modelling is complicated by the fact that the random quantities over which we define the hierarchy are infinite dimensional. We discuss a formal definition of such a hierarchical model. The approach includes a regression at the level of the nonparametric model. For the special case of Dirichlet process mixtures, we develop a Markov chain Monte Carlo scheme to allow efficient implementation of full posterior inference in the given model.