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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Abstract
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 |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| 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 |
| Publisher: | PMLR |
| ISSN: | 1938-7228 |
| 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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