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
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
Awarded by Biotechnology and Biological Sciences Research Council
Awarded by Wellcome Trust
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].