  Frequentist-style large-sample properties of Bayesian posterior
distributions, such as consistency and convergence rates, are important
considerations in nonparametric problems. In this paper we give an analysis of
Bayesian asymptotics based primarily on predictive densities. Our analysis is
unified in the sense that essentially the same approach can be taken to develop
convergence rate results in iid, mis-specified iid, independent non-iid, and
dependent data cases.
