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 im-pair 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. Liter-ature references as well as software descriptions and relevant webpage addresses are provided.