ODE parameter inference using adaptive gradient matching with Gaussian processes

F. Dondelinger, M. Filippone, S. Rogers, D. Husmeier

Research output: Contribution to conferencePaperpeer-review

67 Scopus citations

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 sampling from the joint distribution of the ODE parameters and GP hyperparameters. We demonstrate the efficiency of our adaptive gradient matching technique on three bench- mark systems, and perform a detailed com- parison with the method in Calderhead et al. (2008) and the explicit ODE integration ap- proach, both in terms of parameter inference accuracy and in terms of computational effi- ciency.

Original languageEnglish (US)
Pages216-228
Number of pages13
StatePublished - 2013
Event16th International Conference on Artificial Intelligence and Statistics, AISTATS 2013 - Scottsdale, United States
Duration: Apr 29 2013May 1 2013

Conference

Conference16th International Conference on Artificial Intelligence and Statistics, AISTATS 2013
Country/TerritoryUnited States
CityScottsdale
Period04/29/1305/1/13

Bibliographical note

Publisher Copyright:
Copyright 2013 by the authors.

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Statistics and Probability
  • Artificial Intelligence

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