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