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GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation

Authors
  • He, F
  • Stumpf, M
  • Kleijn, I
  • Roesch, E
  • Hameed, T
  • Ish-Horowicz, J
  • Tankhilevich, E
Publication Date
Jan 27, 2020
Source
Spiral - Imperial College Digital Repository
Keywords
License
Unknown

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 ABC-SMC, or ii) ABC with Gaussian process emulation. The latter significantly reduces the computational cost. Availability and Implementation https://github.com/tanhevg/GpABC.jl Supplementary information Supplementary data are available at Bioinformatics online.

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