Ismael Castillo (Laboratoire de Probabilités, Statistique et Modélisation, Sorbonne Université)

Bayesian nonparametric adaptation with heavy-tailed priors

Abstract. We propose a new strategy for adaptation based on heavy-tailed priors. We illustrate it in a variety of settings, showing in particular adaptation with respect to unknown smoothness and structure parameters in the minimax sense (up to logarithmic factors). We present numerical simulations corroborating the theory. This talk is based on joint works with Sergios Agapiou (Cyprus) and with Paul Egels (Sorbonne, in progress).