Emulation of ODEs with Gaussian Processes

Umberto Noè; Maurizio Filippone; Dirk Husmeier

Emulation of ODEs with Gaussian Processes

Umberto Noè, Maurizio Filippone, Dirk Husmeier

School of Philosophy, Psychology and Language Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

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 language	English
Title of host publication	Proceedings of the 30th International Workshop on Statistical Modelling (IWSM)
Place of Publication	Linz, Austria
Pages	191-194
Volume	2
Publication status	Published - 1 Jul 2015

Cite this

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title = "Emulation of ODEs with Gaussian Processes",

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.",

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AU - Noè, Umberto

AU - Filippone, Maurizio

AU - Husmeier, Dirk

PY - 2015/7/1

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N2 - 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.

AB - 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.

UR - http://eprints.gla.ac.uk/108339/

M3 - Conference contribution

VL - 2

SP - 191

EP - 194

BT - Proceedings of the 30th International Workshop on Statistical Modelling (IWSM)

CY - Linz, Austria

ER -

Emulation of ODEs with Gaussian Processes

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