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.
"Adaptive nonparametric Bayesian inference using location-scale mixture priors." Ann. Statist. 38 (6) 3300 - 3320, December 2010. https://doi.org/10.1214/10-AOS811