Methodology for Bayesian Model Averaging: An Update

The standard practice of selecting a single model from some class of models and then making inferences based on this model ignores model uncertainty. Ignoring model uncertainty can impair predictive performance and lead to overstatement of the strength of evidence via p-values that are too small. Bayesian model averaging provides a coherent approach for accounting for model uncertainty. A variety of methods for implementing Bayesian model averaging have been developed. A brief overview of Bayesian model averaging is provided and recently developed methodology to perform Bayesian model averaging in specific model classes is described. Literature references as well as software descriptions and relevant webpage addresses are provided.