A Julia package for Augmented and Normal Gaussian Processes.
Author
- Théo Galy-Fajou PhD Student at Technical University of Berlin.
Installation
AugmentedGaussianProcesses is a registered package and is symply installed by running
pkg> add AugmentedGaussianProcesses
Basic example
Here is a simple example to start right away :
using AugmentedGaussianProcesses
model = SVGP(compose(SqExponentialKernel(), ScaleTransform(1.0)), LogisticLikelihood(), AnalyticVI(), inducingpoints(KmeansAlg(50), X_train))
train!(model, X_train, y_train; iterations=100)
y_pred = predict_y(model, X_test)
Related Gaussian Processes packages
- GaussianProcesses.jl : General package for Gaussian Processes with many available likelihoods.
- Stheno.jl : Package for Gaussian Process regression.
- AbstractGPs.jl : General package containing base functions for working with GPs.
- GPLikelihoods.jl : Package to define likelihoods for latent GP models.
- ApproximateGPs.jl : Package for variational GPs based on AbstractGPs.jl.
A general comparison between this package is done on Julia GP Package Comparison.
License
AugmentedGaussianProcesses.jl is licensed under the MIT "Expat" license; see LICENSE for the full license text.