A Julia package for Augmented and Normal Gaussian Processes.
- Théo Galy-Fajou PhD Student at Technical University of Berlin.
AugmentedGaussianProcesses is a registered package and is symply installed by running
pkg> add AugmentedGaussianProcesses
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)
- 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.
AugmentedGaussianProcesses.jl is licensed under the MIT "Expat" license; see LICENSE for the full license text.