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 paperPreprint

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
DOIs
Publication statusPublished - 18 Sep 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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