Bayesian nonparametric estimation using the heat kernel
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Mon, 03/06 15:45 |
DOMINIQUE PICARD (Université Paris Diderot) |
Stochastic Analysis Seminar  |
Oxford-Man Institute |
| 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. | |||