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

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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. AVAILABILITY AND IMPLEMENTATION: https://github.com/tanhevg/GpABC.jl.

Original languageEnglish
Pages (from-to)3286-3287
Number of pages2
JournalBioinformatics
Volume36
Issue number10
Early online date5 Feb 2020
DOIs
Publication statusPublished - 1 May 2020

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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

Cite this

Tankhilevich, E., Ish-Horowicz, J., Hameed, T., Roesch, E., Kleijn, I., Stumpf, M. P. H., & He, F. (2020). GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation. Bioinformatics, 36(10), 3286-3287. https://doi.org/10.1093/bioinformatics/btaa078