Bayesian nonparametric estimation using the heat kernel

Stochastic Analysis Seminar Series

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.


Dominique Picard (Denis-Diderot University, Paris)

Monday, June 3, 2013 - 15:45
to 16:45