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Bayesian Model Averaging: A Tutorial

Jennifer A. Hoeting, David Madigan, Adrian E. Raftery and Chris T. Volinsky
Statistical Science
Vol. 14, No. 4 (Nov., 1999), pp. 382-401
Stable URL: http://www.jstor.org/stable/2676803
Page Count: 21
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Bayesian Model Averaging: A Tutorial
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Abstract

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.

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