Bayesian Statistics Seminar
North Carolina State University
presents
Dr. Vinayak Rao
Duke University
"Efficient MCMC for Continuous Time Discrete State Systems"
ABSTRACT
A variety of phenomena are best described using dynamical models which operate on a discrete state space and in continuous time. Examples include Markov jump processes, continuous time Bayesian networks, renewal processes and other point processes, with applications ranging from systems biology, genetics, computing networks and human-computer interactions. Posterior computations typically involve approximations like time discretization and can be computationally intensive. In this talk I will describe recent work on a class of Markov chain Monte Carlo methods that allow efficient computations while still being exact. The core idea is an auxiliary variable Gibbs sampler based on "uniformization", a representation of a continuous time dynamical system as a Markov chain operating over a discrete set of points drawn from a Poisson process.
This is a joint work with Yee Whye Teh (University of Oxford).
Thursday, February, 21, 2013
4:00 - 5:00 pm
1108 SAS Hall