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 journalArticlepeer-review

    10 Citations (Scopus)
    80 Downloads (Pure)


    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 languageEnglish
    Pages (from-to)3286-3287
    Number of pages2
    Issue number10
    Early online date5 Feb 2020
    Publication statusPublished - 1 May 2020

    Bibliographical note

    © The Author(s) 2020. Published by Oxford University Press.

    ASJC Scopus subject areas

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


    Dive into the research topics of 'GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation'. Together they form a unique fingerprint.

    Cite this