Standard statistical practice ignores model uncertainty. Data
analysts typically select a model from some class of models and then proceed as
if the selected model had generated the data. This approach ignores the
uncertainty in model selection, leading to over-confident inferences and
decisions that are more risky than one thinks they are. Bayesian model
averaging (BMA)provides a coherent mechanism for accounting for this model
uncertainty. Several methods for implementing BMA have recently emerged. We
discuss these methods and present a number of examples.In these examples, BMA
provides improved out-of-sample predictive performance. We also provide a
catalogue of currently available BMA software.
Publié le : 1999-11-01
Classification:
Bayesian model averaging,
Bayesian graphical models,
learning,
model uncertainty,
Markov chain Monte Carlo
@article{1009212519,
author = {Hoeting, Jennifer A. and Madigan, David and Raftery, Adrian E. and Volinsky, Chris T.},
title = {Bayesian model averaging: a tutorial (with comments by M. Clyde,
David Draper and E. I. George, and a rejoinder by the authors},
journal = {Statist. Sci.},
volume = {14},
number = {1},
year = {1999},
pages = { 382-417},
language = {en},
url = {http://dml.mathdoc.fr/item/1009212519}
}
Hoeting, Jennifer A.; Madigan, David; Raftery, Adrian E.; Volinsky, Chris T. Bayesian model averaging: a tutorial (with comments by M. Clyde,
David Draper and E. I. George, and a rejoinder by the authors. Statist. Sci., Tome 14 (1999) no. 1, pp. 382-417. http://gdmltest.u-ga.fr/item/1009212519/