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

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

doi:10.1101/769299

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

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

Research output: Working paper/Preprint › Preprint

Abstract

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. 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 ABC-SMC, or ii) ABC with Gaussian process emulation. The latter significantly reduces the computational cost.

Original language	English
Publisher	bioRxiv
DOIs	https://doi.org/10.1101/769299
Publication status	Published - 18 Sept 2019

Bibliographical note

This is the bioRxiv 'Preprint' of an article which on to be published as: Tankhilevich, E., Ish-Horowicz, J., Hameed, T., Roesch, E., Kleijn, I., Stumpf, M.P. and He, F., 2020. GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation. Bioinformatics, 36(10), pp.3286-3287.

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GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation
Tankhilevich, E., Ish-Horowicz, J., Hameed, T., Roesch, E., Kleijn, I., Stumpf, M. P. H. & He, F., 1 May 2020, In: Bioinformatics. 36, 10, p. 3286-3287 2 p.
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title = "GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation",

abstract = "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. 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 ABC-SMC, or ii) ABC with Gaussian process emulation. The latter significantly reduces the computational cost.",

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AU - Stumpf, Michael PH

AU - He, Fei

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

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

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

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