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Philos Trans A Math Phys Eng Sci. 2012 Dec 31;371(1984):20110541. doi: 10.1098/rsta.2011.0541. Print 2013 Feb 13.

Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation.

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Department of Statistical Science, Centre for Computational Statistics and Machine Learning, University College London, Gower Street, London WC1E 6BT, UK.


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.

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