ODE parameter inference using adaptive gradient matching with Gaussian processes

Dondelinger, F., Filippone, M., Rogers, S. and Husmeier, D. (2013) ODE parameter inference using adaptive gradient matching with Gaussian processes. Proceedings of Machine Learning Research, 31, pp. 216-228.

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Parameter inference in mechanistic models based on systems of coupled differential equa- tions is a topical yet computationally chal- lenging problem, due to the need to fol- low each parameter adaptation with a nu- merical integration of the differential equa- tions. Techniques based on gradient match- ing, which aim to minimize the discrepancy between the slope of a data interpolant and the derivatives predicted from the differen- tial equations, offer a computationally ap- pealing shortcut to the inference problem. The present paper discusses a method based on nonparametric Bayesian statistics with Gaussian processes due to Calderhead et al. (2008), and shows how inference in this model can be substantially improved by consistently inferring all parameters from the joint dis- tribution. We demonstrate the efficiency of our adaptive gradient matching technique on three benchmark systems, and perform a de- tailed comparison with the method in Calder- head et al. (2008) and the explicit ODE inte- gration approach, both in terms of parameter inference accuracy and in terms of computa- tional efficiency.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Husmeier, Professor Dirk and Rogers, Dr Simon and Filippone, Dr Maurizio and Dondelinger, Mr Frank
Authors: Dondelinger, F., Filippone, M., Rogers, S., and Husmeier, D.
College/School:College of Science and Engineering > School of Computing Science
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Proceedings of Machine Learning Research
Copyright Holders:Copyright © 2013 The Authors
First Published:First published in Proceedings of Machine Learning Research 31: 216-228
Publisher Policy:Reproduced with the permission of the authors

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
592291Bridging the Gap-EPSRC: Method and software integration for systems biologyDirk HusmeierEngineering & Physical Sciences Research Council (EPSRC)EP/J501463/1M&S - STATISTICS