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Bayesian inference for hybrid discrete-continuous stochastic kinetic models

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.

Publication metadata

Author(s): Sherlock C, Golightly A, Gillespie CS

Publication type: Article

Publication status: Published

Journal: Inverse Problems

Year: 2014

Volume: 30

Issue: 11

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


DOI: 10.1088/0266-5611/30/11/114005


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