GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation
File(s)
Published version
Author(s)
Type
Journal Article
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.
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.
Date Issued
2020-05-15
Date Acceptance
2020-01-27
Citation
Bioinformatics, 2020, 36 (10), pp.3286-3287
ISSN
1367-4803
Publisher
Oxford University Press (OUP)
Start Page
3286
End Page
3287
Journal / Book Title
Bioinformatics
Volume
36
Issue
10
Copyright Statement
© The Author(s) 2020. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Sponsor
Wellcome Trust
Biotechnology and Biological Sciences Research Council (BBSRC)
Wellcome Trust
Wellcome Trust
Identifier
https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa078/5727756
Grant Number
203968/Z/16/Z
BB/N003608/1
215359/Z/19/Z
108908/B/15/Z
Subjects
Bioinformatics
01 Mathematical Sciences
06 Biological Sciences
08 Information and Computing Sciences
Publication Status
Published
Date Publish Online
2020-02-05