BU-1124-MA Default Priors for Robust Bayesian Inference

Robust Bayesian inference involves examining the performance of Bayes rules from a class of prior distributions. However, given a specified class of priors, there is no mechanism for choosing a reasonable default option, that is, a robust prior that is somehow noninformative. We show that, by applying results of classical robustness theory, such priors can be easily defined. These priors display attractive robustness properties and also provide a means for "touring " through a class of priors