Statistics and Data Science Seminar
Raymond Mess / Nick Syring
UIC
Double empirical Bayes for high-dimensional inference / On Bayesian inference without a model
Abstract: This is a special graduate student-organized seminar in which two PhD students (Raymond Mess and Nick Syring) will give 20+
minute talks about their ongoing research. The respective abstracts are below.
(Mess) In this talk, I will introduce the new double empirical Bayes framework, which is based on the use of data to both center and
regularize the prior. An application of this framework to the problem of inference in the sparse (p >> n) linear model will also
be presented.
(Syring) I will introduce a method to obtain Bayesian-like posterior inference
for an unknown parameter without the need for a likelihood. Such a
method makes producing interval estimates straightforward while avoiding
problems that may arise from model misspecification. Finally, I will
discuss an application of this approach to an important problem in medical statistics.
Wednesday November 19, 2014 at 4:00 PM in SEO 636