Bayesian Statistics Seminar
North Carolina State University
presents
Ms. Yeonseung Chung
University of North Carolina - Chapel Hill
"The Local Dirichlet Process"
ABSTRACT
As a generalization of the Dirichlet
process to allow predictor dependence, we propose a local Dirichlet
process (lDP). The lDP provides a prior distribution for a
collection of random probability measures indexed by predictors.
This is accomplished by assigning stick-breaking weights and atoms
to random locations in a predictor space. The probability measure at
a given predictor value is then formulated using the weights and
atoms located in a neighborhood about that predictor value. This
construction results in a marginal Dirichlet process prior for the
random measure at any specific predictor value. Dependence is
induced through local sharing of random components. Theoretical
properties are considered and a blocked Gibbs sampler is proposed
for posterior computation in lDP mixture models. The methods are
illustrated using simulated examples and an epidemiologic
application.
This is a joint work with David B. Dunson at NIEHS
Wednesday, April, 4, 2007
4:00 - 5:00 pm
208 Patterson Hall