Bayesian Model Averaging: A Tutorial
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 in ferences and decisions that are more risky than one thinks they are. Bayesian model averaging (BMA) provides a coherent mechanism for ac counting 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.