Model uncertainty

被引：287

作者：

Clyde, M ^{[1
]}

George, EI

机构：

[1] Duke Univ, Inst Stat & Decis Sci, Durham, NC 27708 USA

[2] Univ Penn, Wharton Sch, Dept Stat, Philadelphia, PA 19104 USA

来源：

STATISTICAL SCIENCE | 2004年 / 19卷 / 01期

关键词：

Bayes factors; classification and regression trees; model averaging; linear and nonparametric regression; objective prior distributions; reversible jump Markov chain Monte Carlo; variable selection;

D O I：

10.1214/088342304000000035

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：

The evolution of Bayesian approaches for model uncertainty over the past decade has been remarkable. Catalyzed by advances in methods and technology for posterior computation, the scope of these methods has widened substantially. Major thrusts of these developments have included new methods for semiautomatic prior specification and posterior exploration. To illustrate key aspects of this evolution, the highlights of some of these developments are described.

引用

页码：81 / 94

页数：14

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