Bayesian nonparametric inference for random distributions and related functions

• 成果类型: Review
• 署名作者: Walker, SG; Damien, P; Laud, PW; Smith, AFM
• 署名单位: Imperial College London; University of Michigan System; University of Michigan; Medical College of Wisconsin; University of London; Queen Mary University London
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/1467-9868.00190
• 发表日期: 1999
• 页码: 485-527
• 关键词: random variate generation; polya tree distributions; dirichlet process prior; censored survival-data; DENSITY-ESTIMATION; regression-models; frailty models; beta-processes; urn schemes; mixtures

摘要

In recent years, Bayesian nonparametric inference, both theoretical and computational, has witnessed considerable advances. However, these advances have not received a full critical and comparative analysis of their scope, impact and limitations in statistical modelling; many aspects of the theory and methods remain a mystery to practitioners and many open questions remain. In this paper, we discuss and illustrate the rich modelling and analytic possibilities that are available to the statistician within the Bayesian nonparametric and/or semiparametric framework.