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Lookup NU author(s): Dr Andrew Golightly,
Dr Colin GillespieORCiD
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We consider the problem of efficiently performing simulation and inference for stochastic kinetic models. Whilst it is possible to work directly with the resulting Markov jump process (MJP), computational cost can be prohibitive for networks of realistic size and complexity. In this paper, we consider an inference scheme based on a novel hybrid simulator that classifies reactions as either 'fast' or 'slow' with fast reactions evolving as a continuous Markov process whilst the remaining slow reaction occurrences are modelled through a MJP with time-dependent hazards. A linear noise approximation (LNA) of fast reaction dynamics is employed and slow reaction events are captured by exploiting the ability to solve the stochastic differential equation driving the LNA. This simulation procedure is used as a proposal mechanism inside a particle MCMC scheme, thus allowing Bayesian inference for the model parameters. We apply the scheme to a simple application and compare the output with an existing hybrid approach and also a scheme for performing inference for the underlying discrete stochastic model.
Author(s): Sherlock C, Golightly A, Gillespie CS
Publication type: Article
Publication status: Published
Journal: Inverse Problems
Print publication date: 01/11/2014
Online publication date: 28/10/2014
Acceptance date: 30/05/2014
ISSN (print): 0266-5611
ISSN (electronic): 1361-6420
Publisher: Institute of Physics Publishing Ltd
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