Bayesian Optimal Adaptive Estimation Using a Sieve Prior

We derive rates of contraction of posterior distributions on non-parametric models resulting from sieve priors. The aim of the study was to provide general conditions to get posterior rates when the parameter space has a general structure, and rate adaptation when the parameter is, for example, a Sobolev class. The conditions employed, although standard in the literature, are combined in a different way. The results are applied to density, regression, nonlinear autoregression and Gaussian white noise models. In the latter we have also considered a loss function which is different from the usual l(2) norm, namely the pointwise loss. In this case it is possible to prove that the adaptive Bayesian approach for the l(2) loss is strongly suboptimal and we provide a lower bound on the rate.