Inference in complex biological systems with Gaussian processes and parallel tempering

Macdonald, B., Dondelinger, F. and Husmeier, D. (2013) Inference in complex biological systems with Gaussian processes and parallel tempering. In: Muggeo, V.M.R., Capursi, V., Boscaino, G. and Lovison, G. (eds.) Proceedings of the 28th International Workshop on Statistical Modelling. Gruppo Istituto Poligrafico Europeo SRL, pp. 673-676. ISBN 9788896251492

Macdonald, B., Dondelinger, F. and Husmeier, D. (2013) Inference in complex biological systems with Gaussian processes and parallel tempering. In: Muggeo, V.M.R., Capursi, V., Boscaino, G. and Lovison, G. (eds.) Proceedings of the 28th International Workshop on Statistical Modelling. Gruppo Istituto Poligrafico Europeo SRL, pp. 673-676. ISBN 9788896251492

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Abstract

Parameter inference in mathematical models of complex biological systems, expressed as coupled ordinary differential equations (ODEs), is a challenging problem. These depend on kinetic parameters, which cannot all be measured and have to be ascertained a different way. However, the computational costs associated with repeatedly solving the ODEs are often staggering, making many techniques impractical. Therefore, aimed at reducing this cost, new concepts using gradient matching have been proposed. This paper combines current adaptive gradient matching approaches, using Gaussian processes, with a parallel tempering scheme, in order to compare 2 different paradigms using the same nonlinear regression method. We use 2 ODE systems to assess our technique, showing an improvement over the recent method in Calderhead et al. (2008).

Item Type:Book Sections
Status:Published
Glasgow Author(s) Enlighten ID:Husmeier, Professor Dirk and Macdonald, Dr Benn and Dondelinger, Mr Frank
Authors: Macdonald, B., Dondelinger, F., and Husmeier, D.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Publisher:Gruppo Istituto Poligrafico Europeo SRL
ISBN:9788896251492
Copyright Holders:Copyright © 2013 Statistical Modelling Society
Publisher Policy:Reproduced with the permission of the editor

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