PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks
Vinayak Rao and Yee Whye Teh
In: UAI 2011, 14 Jul - 17 Jul 2011, Barcelona, Spain.


Markov jump processes and continuous time Bayesian networks are important classes of continuous time dynamical systems. In this paper, we tackle the problem of inferring unobserved paths in these models by introducing a fast auxiliary variable Gibbs sampler. Our approach is based on the idea of uniformization, and sets up a Markov chain over paths by sampling a finite set of virtual jump times and then running a standard hidden Markov model forward filtering backward sampling algorithm over states at the set of extant and virtual jump times. We demonstrate significant computational benefits over a state-of-the-art Gibbs sampler on a number of continuous time Bayesian networks.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:9397
Deposited By:Yee Whye Teh
Deposited On:16 March 2012