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Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks

Abstract:

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 filteringbackward sampling algorithm over st...

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Publication status:
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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Journal:
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011
Pages:
619-626
Publication date:
2011-09-29
URN:
uuid:30a4b3b8-58ed-4353-a438-9183a11100fe
Source identifiers:
353221
Local pid:
pubs:353221

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