Bayesian Statistics Seminar
North Carolina State University
presents
Dr. Sayan Mukherjee
Duke University
"Non-parametric Bayesian simultaneous dimension reduction and regression on manifolds"
ABSTRACT
We formulate a Bayesian non-parametric model for simultaneous dimension reduction and regression as well as inference of graphical models. The proposed model holds for both the classical setting of Euclidean subspaces and the Riemannian setting where the marginal distribution is concentrated on a manifold. The method is designed for the high-dimensional setting where the number of variables far exceed the number of observations. A Markov chain Monte Carlo procedure for inference of model parameters is provided. Properties of the method and its utility are elucidated using simulations and real data.
This is a joint work with K. Mao, Q. Wu and F. Liang. The paper is available online.
Tuesday, September, 23, 2008
4:00 - 5:00 pm
208 Patterson Hall