1999 BayesianModelAveragingATutorial

• (Hoeting et al., 1999) ⇒ Jennifer A. Hoeting, David Madigan, Adrian E. Raftery, and Chris T. Volinsky. (1999). “Bayesian Model Averaging: A Tutorial.” In: Statistical science.

Subject Headings: Bayesian Graphical Model, Bayesian Model Averaging.

Cited By

• http://scholar.google.com/scholar?q=%22Bayesian+model+averaging%3A+A+tutorial%22+1999

Quotes

Author Keywords

• Bayesian model averaging; Bayesian graphical models; learning; model uncertainty; Markov chain Monte Carlo.

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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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1999 BayesianModelAveragingATutorial	David Madigan Jennifer A. Hoeting Adrian E. Raftery Chris Volinsky			Bayesian Model Averaging: A Tutorial						1999