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: Practice-Based and Non-textual ResearchWeb publication/site

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 languageEnglish
PublisherbioRxiv
Media of outputOnline
DOIs
Publication statusPublished - 18 Sep 2019

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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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    Cite this

    Tankhilevich, E. (Author), Ish-Horowicz, J. (Author), Hameed, T. (Author), Roesch, E. (Author), Kleijn, I. (Author), Stumpf, M. PH. (Author), & He, F. (Author). (2019). GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation. Web publication/site, bioRxiv. https://doi.org/10.1101/769299