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(X_train,y_train,SqExponentialKernel(1.0),LogisticLikelihood(),AnalyticVI(),50)
train!(model,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
• AbstractGP.jl : General package containing base functions for working with GPs
• GPLikelihoods.jl : Package to define likelihoods for latent GP models

A general comparison between this package is done on Julia GP Package Comparison. Benchmark evaluations may come later.

License

AugmentedGaussianProcesses.jl is licensed under the MIT "Expat" license; see LICENSE for the full license text.