  We use rescaled Gaussian processes as prior models for functional parameters
in nonparametric statistical models. We show how the rate of contraction of the
posterior distributions depends on the scaling factor. In particular, we
exhibit rescaled Gaussian process priors yielding posteriors that contract
around the true parameter at optimal convergence rates. To derive our results
we establish bounds on small deviation probabilities for smooth stationary
Gaussian processes.
