  We study the rate of Bayesian consistency for hierarchical priors consisting
of prior weights 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 sure assertions. As
an application we study log spline densities with a finite number of models and
obtain that the Bayes procedure achieves the optimal minimax rate
$n^{-\gamma/(2\gamma+1)}$ of convergence if the true density of the
observations belongs to the H\"{o}lder space $C^{\gamma}[0,1]$. This
strengthens a result in [1; 2]. We also study consistency of posterior
distributions of the model index and give conditions ensuring that the
posterior distributions concentrate their masses near the index of the best
model.
