GpABC
provides algorithms for likelihood - free parameter inference and model selection using Approximate Bayesian Computation (ABC). Two sets of algorithms are available:
GpABC
offers Gaussian Process Regression (GPR) as an emulator, but custom emulators can also be used. GPR can also be used standalone, for any regression task.
Stochastic models, that don't conform to Gaussian Process Prior assumption, are supported via Linear Noise Approximation (LNA).
GpABC
can be installed using the Julia package manager.
From the Julia REPL, type ]
to enter the Pkg REPL mode and run
pkg> add GpABC
If you are using GpABC in research, please cite our paper:
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
https://www.biorxiv.org/content/10.1101/769299v1
doi: 10.1093/bioinformatics/btaa078
@article{10.1093/bioinformatics/btaa078,
author = {Tankhilevich, Evgeny and Ish-Horowicz, Jonathan and Hameed, Tara and Roesch, Elisabeth and Kleijn, Istvan and Stumpf, Michael P H and He, Fei},
title = "{GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation}",
journal = {Bioinformatics},
year = {2020},
month = {02},
abstract = "{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. Supplementary data are available at Bioinformatics online.}",
issn = {1367-4803},
doi = {10.1093/bioinformatics/btaa078},
url = {https://doi.org/10.1093/bioinformatics/btaa078},
note = {btaa078},
eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa078/32353462/btaa078.pdf},
}
Optim
- for training Gaussian Process hyperparameters.Distributions
- probability distributions.Distances
- distance functionsOrdinaryDiffEq
- for solving ODEs for LNA, and also used throughout the examples for model simulation (ODEs and SDEs)ForwardDiff
- automatic differentiation is also used by LNAPlotUtils
, RecipesBase
, Colors
, KernelDensity
- for plotting figures