Event Date
SPEAKER: Nicholas Syring, Postdoctoral Lecturer in Statistics, Washington University in Saint Louis
TITLE: “Gibbs posterior distributions”
ABSTRACT: Bayesian methods provide a standard framework for statistical inference in which prior beliefs about a population under study are combined with evidence provided by data to produce revised posterior beliefs. As with all likelihood-based methods, Bayesian methods may present drawbacks stemming from model misspecification and over-parametrization. A generalization of Bayesian posteriors, called Gibbs posteriors, link the data and population parameters of interest via a loss function rather than a likelihood, thereby avoiding these potential difficulties. At the same time, Gibbs posteriors retain the prior-to-posterior updating of beliefs. We will illustrate the advantages of Gibbs methods in examples and highlight newly developed strategies to analyze the large-sample properties of Gibbs posterior distributions.
DATE: Friday, January 31st, 10:10am
LOCATION: MSB 1147, Colloquium Room
REFRESHMENTS: 10:00am, MSB 1147