Date:
Monday, November 7, 2016, 4:15pm to 5:15pm
Location:
Science Center Rm. 300H
Bayesian statistics without likelihood
Bayesian analysis of high-dimensional graphical models often leads to posterior distributions that are computationally intractable. Similar issues arise with other classes of statistical models. In this talk I will advocate the use of more general loss functions in the Bayesian machinery. The idea is not new, but I will present some new results on the contraction properties of the resulting quasi-posterior distributions in the high-dimensional regime. Computational aspects will also be discussed if time permits.