On adaptive Bayesian inference

Abstract

We study the rate of Bayesian consistency for hierarchical priors consisting of priorweights on a model index set and a prior on a density model for each choice of model index. Ghosal, Lember and Van der Vaart [2] have obtained general in-probability theorems on the rate of convergence of the resulting posterior distributions. We extend their results to almost sureassertions. As an application we study log spline densities with a finite number of models andobtain that the Bayes procedure achieves the optimal minimax rate n−γ/(2γ+1) of convergence if the true density of the observations belongs to the H¨older space Cγ[0, 1]. This strengthens a result in [1; 2]. We also study consistency of posterior distributions of the model index and give conditions ensuring that theposterior distributions concentrate their masses near the index of the best model. © 2008, Institute of Mathematical Statistics. All rights reserved.