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Fast MCMC sampling for Markov jump processes and extensions

Abstract:

Markov jump processes (or continuous-time Markov chains) are a simple and important class of continuous-time dynamical systems. In this paper, we tackle the problem of simulating from the posterior distribution over paths in these models, given partial and noisy observations. Our approach is an auxiliary variable Gibbs sampler, and is based on the idea of uniformization. This sets up a Markov chain over paths by alternately sampling a finite set of virtual jump times given the current path, a...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Gatsby Charitable Foundation More from this funder
Publisher:
Journal of Machine Learning Research Publisher's website
Journal:
Journal of Machine Learning Research Journal website
Volume:
14
Pages:
3207-3232
Publication date:
2013
EISSN:
1533-7928
ISSN:
1532-4435
URN:
uuid:2cd273d2-c61e-4c2e-bbb5-69f94d9e1710
Source identifiers:
364358
Local pid:
pubs:364358

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