- (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.
- Bayesian model averaging; Bayesian graphical models; learning; model uncertainty; Markov chain Monte Carlo.
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.
|1999 BayesianModelAveragingATutorial||Jennifer A. Hoeting|
Adrian E. Raftery
|Bayesian Model Averaging: A Tutorial||1999|