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February 2004 Model Uncertainty
Merlise Clyde, Edward I. George
Statist. Sci. 19(1): 81-94 (February 2004). DOI: 10.1214/088342304000000035


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.


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Merlise Clyde. Edward I. George. "Model Uncertainty." Statist. Sci. 19 (1) 81 - 94, February 2004.


Published: February 2004
First available in Project Euclid: 14 July 2004

zbMATH: 1062.62044
MathSciNet: MR2082148
Digital Object Identifier: 10.1214/088342304000000035

Keywords: Bayes factors , classification and regression trees , linear and nonparametric regression , model averaging , objective prior distributions , reversible jump Markov chain Monte Carlo , Variable selection

Rights: Copyright © 2004 Institute of Mathematical Statistics


Vol.19 • No. 1 • February 2004
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