Bayesian nonparametric inference for random distributions and related functions

被引：138

作者：

Walker, SG

Damien, P

Laud, PW

Smith, AFM

机构：

[1] Univ London Imperial Coll Sci Technol & Med, Dept Math, London SW7 2BZ, England

[2] Univ Michigan, Ann Arbor, MI 48109 USA

[3] Med Coll Wisconsin, Milwaukee, WI 53226 USA

[4] Queen Mary Univ London, London E1 4NS, England

来源：

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY | 1999年 / 61卷

关键词：

Dirichlet process; exchangeability; Levy process; neutral to the right process; Polya tree; survival model;

D O I：

10.1111/1467-9868.00190

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

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.

引用

页码：485 / 527

页数：43

共 139 条

[1] Wavelet thresholding via a Bayesian approach [J].