Adaptive nonparametric Bayesian inference using location-scale mixture   priors
  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. 
R. de Jonge, J. H. van Zanten
math.ST stat.TH