Inference in Nonlinear Differential Equations

Niu, M., Filippone, M., Husmeier, D. and Rogers, S. (2015) Inference in Nonlinear Differential Equations. In: 30th International Workshop on Statistical Modelling, Linz, Austria, 06-10 Jul 2015, pp. 187-190.

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Parameter inference in mechanistic models of coupled differential equations is a challenging problem. We propose a new method using kernel ridge regression in Reproducing Kernel Hilbert Spaces (RKHS). A three-step gradient matching algorithm is developed and applied to a realistic biochemical model.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Husmeier, Professor Dirk and Rogers, Dr Simon and Filippone, Dr Maurizio and Niu, Dr Mu
Authors: Niu, M., Filippone, M., Husmeier, D., and Rogers, S.
College/School:College of Science and Engineering > School of Computing Science
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Copyright Holders:Copyright © 2015 The Authors
Publisher Policy:Reproduced with the permission of the authors
Data DOI:10.5525/gla.researchdata.284

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
633291Computational inference in systems biologyDirk HusmeierEngineering & Physical Sciences Research Council (EPSRC)EP/L020319/1M&S - STATISTICS