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Gaussian Processes for Machine Learning in Julia (@JuliaGaussianProcesses)

JuliaGaussianProcesses

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Stheno.jl

Probabilistic Programming with Gaussian processes in Julia

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

KernelSpectralDensities.jl

RandomFourierFeatures.jl

[WIP] Random Fourier Feature approximations for KernelFunctions.jl

EasyGPs.jl

Easy automatic fitting of JuliaGP models

AbstractGPsMakie.jl

Plots of Gaussian processes with AbstractGPs and Makie

JuliaGPsDocs.jl

General setup to generate examples for the JuliaGP packages

JuliaGaussianProcesses.github.io

Website for the JuliaGaussianProcesses organisation and its packages