Bayesian Model Averaging

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. KEYWORDS: Bayesian model averaging; Bayesian graphical models; Learning; Model uncertainty; Markov chain Monte Carlo Research supported in part by the U.S. National Science Foundation and the U.S. Office of Naval Research (N00014-91-J-1014). The authors are grateful to David Lewis and Ro..