Statistics Seminar: "Gibbs posterior distributions"

Abstract: Gibbs posterior distributions are the natural analog to Bayesian posteriors when the parameter of interest is defined via a loss function rather than a likelihood.  One construction of Gibbs posteriors is given in Bissiri et al. (2016) using decision theory and motivated by a coherence principle.  Besides being a principled method of updating beliefs Gibbs posteriors can exhibit favorable frequentist properties under general conditions.  We will explore some sufficient conditions for posterior concentration and review examples.

Host: Likai Chen