Postdoctoral Researcher, NCAR
Monday, 29 September 2003
Perfect sampling algorithms are Markov Chain Monte Carlo methods
without statistical error - if applicable, they enable exact simulation
from the stationary distribution on a Markov chain.
an introduction to the general idea of perfect sampling and discuss
current challenges in this area. We present some advances including
"slice coupling" and variants on the so-called IMH-algorithm.
Applications of these
advances are given to Bayesian variable selection where one
needs to sample from the posterior distribution of the Bayesian
model. We employ slice coupling methods and the bounded IMH-algorithm
to specify perfect sampling algorithms for selection models
in a linear regression setup.
be served at 3:45 p.m. in Room 008 of the Statistics Building