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Bayesian model averaging: A tutorial
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DOI：10.1214/ss/1009212519.png)
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Abstract
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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.
Keywords:
Bayesian model averaging
Bayesian graphical models
learning
model uncertainty
Markov chain Monte Carlo
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