GpABC swMATH ID: 43392 Software Authors: Tankhilevich, E.; Ish-Horowicz, J.; Hameed, T.; Roesch, E.; Kleijn, I.; Stumpf, M.P.H.; He, F. Description: GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation. 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. Homepage: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7214045/ Source Code: https://github.com/tanhevg/GpABC.jl Dependencies: Julia Related Software: PMTK; torchdiffeq; NeuralPDE.jl; ANODEs; sugsvarsel; Flux; DiffEqFlux; DifferentialEquations.jl Cited in: 1 Document Cited by 3 Authors 1 Rackauckas, Christopher 1 Roesch, Elisabeth 1 Stumpf, Michael P. H. Cited in 1 Serial 1 Statistical Applications in Genetics and Molecular Biology Cited in 3 Fields 1 Numerical analysis (65-XX) 1 Computer science (68-XX) 1 Biology and other natural sciences (92-XX) Citations by Year