Gaussian Processes for Machine Learning in Julia's repositories
KernelFunctions.jl
Julia package for kernel functions for machine learning
AbstractGPs.jl
Abstract types and methods for Gaussian Processes.
TemporalGPs.jl
Fast inference for Gaussian processes in problems involving time. Partly built on results from https://proceedings.mlr.press/v161/tebbutt21a.html
ParameterHandling.jl
Foundational tooling for handling collections of parameters in models
GPLikelihoods.jl
Provides likelihood functions for Gaussian Processes.
ApproximateGPs.jl
Approximations for Gaussian processes: sparse variational inducing point approximations, Laplace approximation, ...
BayesianLinearRegressors.jl
Bayesian Linear Regression in Julia
AugmentedGPLikelihoods.jl
Provide all functions needed to work with augmented likelihoods (conditionally conjugate with Gaussians)
InducingPoints.jl
Package for different inducing points selection methods
LinearMixingModels.jl
http://proceedings.mlr.press/v119/bruinsma20a.html
RandomFourierFeatures.jl
[WIP] Random Fourier Feature approximations for KernelFunctions.jl
AbstractGPsMakie.jl
Plots of Gaussian processes with AbstractGPs and Makie
EasyGPs.jl
Easy automatic fitting of JuliaGP models
JuliaGaussianProcesses.github.io
Website for the JuliaGaussianProcesses organisation and its packages
JuliaGPsDocs.jl
General setup to generate examples for the JuliaGP packages
ScalarKernelFunctions.jl
Kernel functions optimized for 1d input