PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Gaussian Process Modulated Renewal Processes
Vinayak Rao and Yee Whye Teh
In: NIPS 2011, 12 Dec - 17 Dec 2011, Granada, Spain.

Abstract

Renewal processes are generalizations of the Poisson process on the real line whose intervals are drawn i.i.d. from some distribution. Modulated renewal processes allow these interevent distributions to vary with time, allowing the introduction of nonstationarity. In this work, we take a nonparametric Bayesian approach, modelling this nonstationarity with a Gaussian process. Our approach is based on the idea of uniformization, which allows us to draw exact samples from an otherwise intractable distribution. We develop a novel and efficient MCMC sampler for posterior inference. In our experiments, we test these on a number of synthetic and real datasets.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:9398
Deposited By:Yee Whye Teh
Deposited On:16 March 2012