BAYESIAN DENSITY-ESTIMATION AND INFERENCE USING MIXTURES

• 成果类型: Article
• 署名作者: ESCOBAR, MD; WEST, M
• 署名单位: University of Toronto; Duke University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2291069
• 发表日期: 1995
• 页码: 577-588
• 关键词: nonparametric problems; dirichlet processes; computation

摘要

We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dirichlet processes. These models provide natural settings for density estimation and are exemplified by special eases where data are modeled as a sample from mixtures of normal distributions. Efficient simulation methods are used to approximate various prior, posterior, and predictive distributions. This allows for direct inference on a variety of practical issues, including problems of local versus global smoothing, uncertainty about density estimates, assessment of modality, and the inference on the numbers of components. Also, convergence results are established for a general class of normal mixture models.