Posterior consistency in Bayesian inference with exponential priors

We consider the problem of recovering the underlying truth in a nonparametric Bayesian inference setting with p-exponential priors. These priors are a class of infinite dimensional product measures with tails between exponential and Gaussian. We will discuss some results on the rates of contraction of the posterior around the truth for the white noise and density estimation models.

This is joint work with S. Agapiou and T. Helin.

invited by Jana de Wiljes

***Due to the current pandemic this colloquium will be conducted online. We invite you to join and spread the news. We will send out an invitation for a zoom meeting via our email list. If you are not on the mail list already, please send an email up front to liv.heinecke[at]uni-potsdam.de ***