  Bounds on Bayesian posterior convergence rates, assuming the prior satisfies
both local and global support conditions, are now readily available. In this
paper we explore, in the context of density estimation, Bayesian convergence
rates assuming only local prior support conditions. Our results give optimal
rates under minimal conditions using very simple arguments.
