# Adaptive nonparametric Bayesian inference using location-scale mixture priors

@article{Jonge2010AdaptiveNB, title={Adaptive nonparametric Bayesian inference using location-scale mixture priors}, author={Robert de Jonge and J. H. van Zanten}, journal={Annals of Statistics}, year={2010}, volume={38}, pages={3300-3320} }

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.

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