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.
Publié le : 2004-02-14
Classification:
Bayes factors,
classification and regression trees,
model averaging,
linear and nonparametric regression,
objective prior distributions,
reversible jump Markov chain Monte Carlo,
variable selection
@article{1089808274,
author = {Clyde, Merlise and George, Edward I.},
title = {Model Uncertainty},
journal = {Statist. Sci.},
volume = {19},
number = {1},
year = {2004},
pages = { 81-94},
language = {en},
url = {http://dml.mathdoc.fr/item/1089808274}
}
Clyde, Merlise; George, Edward I. Model Uncertainty. Statist. Sci., Tome 19 (2004) no. 1, pp. 81-94. http://gdmltest.u-ga.fr/item/1089808274/