  The goal of this paper is to provide theorems on convergence rates of
posterior distributions that can be applied to obtain good convergence rates in
the context of density estimation as well as regression. We show how to choose
priors so that the posterior distributions converge at the optimal rate without
prior knowledge of the degree of smoothness of the density function or the
regression function to be estimated.
