Bagging is a method of obtaining more robust predictions when the model class under consideration is unstable with respect to the data, i.e., small changes in the data can cause the predicted values to change significantly. In this paper, we introduce a Bayesian version of bagging based on the Bayesian bootstrap. The Bayesian bootstrap resolves a theoretical problem with ordinary bagging and often results in more efficient estimators. We show how model averaging can be combined within the Bayesian bootstrap and illustrate the procedure with several examples.

The manuscript is available in postscript and pdf formats.