  We study the performance of nonparametric Bayes procedures for
one-dimensional diffusions with periodic drift. We improve existing convergence
rate results for Gaussian process (GP) priors with fixed hyper parameters.
Moreover, we exhibit several possibilities to achieve adaptation to smoothness.
We achieve this by considering hierarchical procedures that involve either a
prior on a multiplicative scaling parameter, or a prior on the regularity
parameter of the GP.
