  This paper introduces a new approach to the study of rates of convergence for
posterior distributions. It is a natural extension of a recent approach to the
study of Bayesian consistency. In particular, we improve on current rates of
convergence for models including the mixture of Dirichlet process model and the
random Bernstein polynomial model.
