Nonparametric adaptation with heavy–tailed priors in statistics and machine learning

This thesis studies the frequentist properties of Bayesian methods for nonparametric inference. Focusing in particular on regression problems, we construct Bayesian procedures that automatically adapt to unknown characteristics of the regression function, while avoiding explicit model selection or h