Emulation of ODEs with Gaussian Processes

Umberto Noè, Maurizio Filippone, Dirk Husmeier

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Inference in nonlinear ordinary differential equations (ODEs) is challenging due to the high computational complexity of the numerical integration. In the present paper, we explore an emulation-based approach for approximating the likelihood, based on Gaussian processes, with the objective to reduce the number of numerical integration steps. We assess the viability of the scheme on the Lotka-Volterra model of predator-prey interactions.
Original languageEnglish
Title of host publicationProceedings of the 30th International Workshop on Statistical Modelling (IWSM)
Place of PublicationLinz, Austria
Pages191-194
Volume2
Publication statusPublished - 1 Jul 2015

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