Statistics Seminar: James P. Hobert -- Improving the Data Augmentation Algorithm

James P. Hobert 
Department of Statistics 
University of Florida

Location: Carslaw 173

Time:  2pm Friday, June 10, 2011

Improving the Data Augmentation Algorithm

Abstract: 
After a brief review of the data augmentation (DA) algorithm, I will
introduce a simple modification that results in a new Markov chain that
remains reversible with respect to the target distribution.  We call
this modification the "sandwich algorithm" because it involves an extra
move that is sandwiched between the two conditional draws of the DA
algorithm.  General results will be presented showing that the sandwich
algorithm is at least as good as the underlying DA algorithm in terms of
both efficiency and convergence rate.  An example will be used to
demonstrate that, in practice, massive gains are possible.  (This is
joint work with K. Khare, D. Marchev and V. Roy.)