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.
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.