I will present a general technique for obtaining Bayes risk lower bounds for arbitrary priors in standard decision theoretic problems. The method leads to generalizations of a variety of classical minimax bounds. I will describe some applications to minimaxity and admissibility. This is based on joint work with Xi Chen and Yuchen Zhang.