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Inference for reaction networks using the linear noise approximation

Fearnhead, Paul and Giagos, Vasileios and Sherlock, Christopher (2014) Inference for reaction networks using the linear noise approximation. Biometrics, 70. pp. 457-466. ISSN 0006-341X

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Abstract

We consider inference for the reaction rates in discretely observed networks such as those found in models for systems biology, population ecology and epidemics. Most such networks are neither slow enough nor small enough for inference via the true state-dependent Markov jump process to be feasible. Typically, inference is conducted by approximating the dynamics through an ordinary differential equation (ODE), or a stochastic differential equation (SDE). The former ignores the stochasticity in the true model, and can lead to inaccurate inferences. The latter is more accurate but is harder to implement as the transition density of the SDE model is generally unknown. The Linear Noise Approximation (LNA) is a first order Taylor expansion of the approximating SDE about a deterministic solution. It can be viewed as a compromise between the ODE and SDE models. It is a stochastic model, but discrete time transition probabilities for the LNA are available through the solution of a series of ordinary differential equations. We describe how the LNA can be used to perform inference for a general class of reaction networks; evaluate the accuracy of such an approach; and show how and when this approach is either statistically or computationally more efficient than ODE or SDE methods.

Item Type: Article
Journal or Publication Title: Biometrics
Uncontrolled Keywords: Linear noise approximation ; Reaction network ; Google Flu Trends
Subjects:
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 69408
Deposited By: ep_importer_pure
Deposited On: 07 May 2014 14:28
Refereed?: Yes
Published?: Published
Last Modified: 12 Dec 2017 04:44
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/69408

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