Bayesian nonparametric estimation using the heat kernel

3 June 2013
15:45
DOMINIQUE PICARD
Abstract
Convergence of the Bayes posterior measure is considered in canonical statistical settings (like density estimation or nonparametric regression) where observations sit on a geometrical object such as a compact manifold, or more generally on a compact metric space verifying some conditions. A natural geometric prior based on randomly rescaled solutions of the heat equation is considered. Upper and lower bound posterior contraction rates are derived.
  • Stochastic Analysis Seminar