GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation

Evgeny Tankhilevich; Jonathan Ish-Horowicz; Tara Hameed; Elisabeth Roesch; Istvan Kleijn; Michael P H Stumpf; Fei He

doi:10.1093/bioinformatics/btaa078

GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation

Evgeny Tankhilevich, Jonathan Ish-Horowicz, Tara Hameed, Elisabeth Roesch, Istvan Kleijn, Michael P H Stumpf, Fei He

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

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Abstract

Motivation: Approximate Bayesian computation (ABC) is an important framework within which to infer the structure and parameters of a systems biology model. It is especially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelihood functions are intractable. However, the associated computational cost often limits ABC to models that are relatively quick to simulate in practice. Results: We here present a Julia package, GpABC, that implements parameter inference and model selection for deterministic or stochastic models using (i) standard rejection ABC or sequential Monte Carlo ABC or (ii) ABC with Gaussian process emulation. The latter significantly reduces the computational cost.

Original language	English
Pages (from-to)	3286-3287
Number of pages	2
Journal	Bioinformatics
Volume	36
Issue number	10
Early online date	5 Feb 2020
DOIs	https://doi.org/10.1093/bioinformatics/btaa078
Publication status	Published - 1 May 2020

Bibliographical note

ASJC Scopus subject areas

Statistics and Probability
Biochemistry
Molecular Biology
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics

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10.1093/bioinformatics/btaa078Licence: CC BY

Tankhilevich_et_al_GpABCFinal published version, 1.24 MBLicence: CC BY

Cite this

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abstract = "Motivation: Approximate Bayesian computation (ABC) is an important framework within which to infer the structure and parameters of a systems biology model. It is especially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelihood functions are intractable. However, the associated computational cost often limits ABC to models that are relatively quick to simulate in practice. Results: We here present a Julia package, GpABC, that implements parameter inference and model selection for deterministic or stochastic models using (i) standard rejection ABC or sequential Monte Carlo ABC or (ii) ABC with Gaussian process emulation. The latter significantly reduces the computational cost.",

author = "Evgeny Tankhilevich and Jonathan Ish-Horowicz and Tara Hameed and Elisabeth Roesch and Istvan Kleijn and Stumpf, {Michael P H} and Fei He",

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AU - Tankhilevich, Evgeny

AU - Ish-Horowicz, Jonathan

AU - Hameed, Tara

AU - Roesch, Elisabeth

AU - Kleijn, Istvan

AU - Stumpf, Michael P H

AU - He, Fei

PY - 2020/5/1

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N2 - Motivation: Approximate Bayesian computation (ABC) is an important framework within which to infer the structure and parameters of a systems biology model. It is especially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelihood functions are intractable. However, the associated computational cost often limits ABC to models that are relatively quick to simulate in practice. Results: We here present a Julia package, GpABC, that implements parameter inference and model selection for deterministic or stochastic models using (i) standard rejection ABC or sequential Monte Carlo ABC or (ii) ABC with Gaussian process emulation. The latter significantly reduces the computational cost.

AB - Motivation: Approximate Bayesian computation (ABC) is an important framework within which to infer the structure and parameters of a systems biology model. It is especially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelihood functions are intractable. However, the associated computational cost often limits ABC to models that are relatively quick to simulate in practice. Results: We here present a Julia package, GpABC, that implements parameter inference and model selection for deterministic or stochastic models using (i) standard rejection ABC or sequential Monte Carlo ABC or (ii) ABC with Gaussian process emulation. The latter significantly reduces the computational cost.

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GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation

Abstract

Bibliographical note

ASJC Scopus subject areas

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