- Statist. Sci.
- Volume 14, Number 4 (1999), 382-417.
Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors
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.
Statist. Sci., Volume 14, Number 4 (1999), 382-417.
First available in Project Euclid: 24 December 2001
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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. 14 (1999), no. 4, 382--417. doi:10.1214/ss/1009212519. https://projecteuclid.org/euclid.ss/1009212519