Statistics Papers
Title
Document Type
Journal Article
Date of this Version
2004
Publication Source
Statistical Science
Volume
19
Issue
1
Start Page
81
Last Page
94
DOI
10.1214/088342304000000035
Abstract
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.
Keywords
Bayes factors, classification and regression trees, model averaging, linear and nonparametric regression, objective prior distributions, reversible jump Markov chain Monte Carlo, variable selection
Recommended Citation
Clyde, M., & George, E. I. (2004). Model Uncertainty. Statistical Science, 19 (1), 81-94. http://dx.doi.org/10.1214/088342304000000035
Date Posted: 27 November 2017
This document has been peer reviewed.