Probabilistic Programming with Gaussian processes in Julia

Julia package for kernel functions for machine learning

Abstract types and methods for Gaussian Processes.

Fast inference for Gaussian processes in problems involving time. Partly built on results from https://proceedings.mlr.press/v161/tebbutt21a.html

Foundational tooling for handling collections of parameters in models

Provides likelihood functions for Gaussian Processes.

Approximations for Gaussian processes: sparse variational inducing point approximations, Laplace approximation, ...

Bayesian Linear Regression in Julia

Provide all functions needed to work with augmented likelihoods (conditionally conjugate with Gaussians)

Package for different inducing points selection methods

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Http://proceedings.mlr.press/v119/bruinsma20a.html

[WIP] Random Fourier Feature approximations for KernelFunctions.jl

Plots of Gaussian processes with AbstractGPs and Makie

Kernel functions optimized for 1d input