Journal article

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

Bioinformatics | Oxford University Press (OUP) | Published : 2020

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 reduc..

View full abstract

University of Melbourne Researchers

Grants

Awarded by Biotechnology and Biological Sciences Research Council


Awarded by Wellcome Trust


Funding Acknowledgements

This work was supported by Biotechnology and Biological Sciences Research Council [BB/N003608/1] and by Wellcome Trust PhD awards to J.I.-H., T.H. and I.K [108908/B/15/Z, 215358/Z/19/Z, 215359/Z/19/Z, 203968/Z/16/Z].