  We study location-scale mixture priors for nonparametric statistical
problems, including multivariate regression, density estimation and
classification. We show that a rate-adaptive procedure can be obtained if the
prior is properly constructed. In particular, we show that adaptation is
achieved if a kernel mixture prior on a regression function is constructed
using a Gaussian kernel, an inverse gamma bandwidth, and Gaussian mixing
weights.
