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Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation
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Stathopoulos, Vassilios and Girolami, Mark (2012) Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Volume 371 (Number 1984). Article number 20110541. doi:10.1098/rsta.2011.0541 ISSN 1364-503X.
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Official URL: http://dx.doi.org/10.1098/rsta.2011.0541
Abstract
Bayesian analysis for Markov jump processes (MJPs) is a non-trivial and challenging problem. Although exact inference is theoretically possible, it is computationally demanding, thus its applicability is limited to a small class of problems. In this paper, we describe the application of Riemann manifold Markov chain Monte Carlo (MCMC) methods using an approximation to the likelihood of the MJP that is valid when the system modelled is near its thermodynamic limit. The proposed approach is both statistically and computationally efficient whereas the convergence rate and mixing of the chains allow for fast MCMC inference. The methodology is evaluated using numerical simulations on two problems from chemical kinetics and one from systems biology.
Item Type: | Journal Article | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Journal or Publication Title: | Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences | ||||
Publisher: | The Royal Society Publishing | ||||
ISSN: | 1364-503X | ||||
Official Date: | 31 December 2012 | ||||
Dates: |
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Volume: | Volume 371 | ||||
Number: | Number 1984 | ||||
Article Number: | Article number 20110541 | ||||
DOI: | 10.1098/rsta.2011.0541 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
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