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Diagnostics for assessing the linear noise and moment closure approximations

Author

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  • Gillespie Colin S.

    (Newcastle University – School of Mathematics and Statistics, Newcastle, United Kingdom of Great Britain and Northern Ireland)

  • Golightly Andrew

    (Newcastle University – School of Mathematics and Statistics, Newcastle, United Kingdom of Great Britain and Northern Ireland)

Abstract

Solving the chemical master equation exactly is typically not possible, so instead we must rely on simulation based methods. Unfortunately, drawing exact realisations, results in simulating every reaction that occurs. This will preclude the use of exact simulators for models of any realistic size and so approximate algorithms become important. In this paper we describe a general framework for assessing the accuracy of the linear noise and two moment approximations. By constructing an efficient space filling design over the parameter region of interest, we present a number of useful diagnostic tools that aids modellers in assessing whether the approximation is suitable. In particular, we leverage the normality assumption of the linear noise and moment closure approximations.

Suggested Citation

  • Gillespie Colin S. & Golightly Andrew, 2016. "Diagnostics for assessing the linear noise and moment closure approximations," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(5), pages 363-379, October.
    • Handle: RePEc:bpj:sagmbi:v:15:y:2016:i:5:p:363-379:n:1
    DOI: 10.1515/sagmb-2014-0071
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    References listed on IDEAS

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    1. Golightly, A. & Wilkinson, D.J., 2008. "Bayesian inference for nonlinear multivariate diffusion models observed with error," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1674-1693, January.
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