https://github.com/juliagaussianprocesses/augmentedgplikelihoods.jl

Provide all functions needed to work with augmented likelihoods (conditionally conjugate with Gaussians)
https://github.com/juliagaussianprocesses/augmentedgplikelihoods.jl

Last synced: 29 days ago
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Provide all functions needed to work with augmented likelihoods (conditionally conjugate with Gaussians)

• Host: GitHub
• URL: https://github.com/juliagaussianprocesses/augmentedgplikelihoods.jl
• Owner: JuliaGaussianProcesses
• License: mit
• Created: 2021-12-03T10:03:26.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2024-07-20T13:54:27.000Z (almost 2 years ago)
• Last Synced: 2025-02-20T22:06:03.921Z (over 1 year ago)
• Language: Julia
• Homepage:
• Size: 482 MB
• Stars: 20
• Watchers: 2
• Forks: 1
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# AugmentedGPLikelihoods

[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://JuliaGaussianProcesses.github.io/AugmentedGPLikelihoods.jl/stable)

[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://JuliaGaussianProcesses.github.io/AugmentedGPLikelihoods.jl/dev)

[![Build Status](https://github.com/JuliaGaussianProcesses/AugmentedGPLikelihoods.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/JuliaGaussianProcesses/AugmentedGPLikelihoods.jl/actions/workflows/CI.yml?query=branch%3Amain)

[![Coverage](https://codecov.io/gh/JuliaGaussianProcesses/AugmentedGPLikelihoods.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/JuliaGaussianProcesses/AugmentedGPLikelihoods.jl)

[![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle)

[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)

[![DOI](https://zenodo.org/badge/434548515.svg)](https://zenodo.org/badge/latestdoi/434548515)

Provides all necessary functions to work with augmented likelihoods for Gaussian Processes and more, check out the docs for more info.

For a global overview over the approach, see the PhD thesis [Latent variable augmentation for approximate Bayesian inference,

applications for Gaussian processes](https://depositonce.tu-berlin.de/items/02f8e83d-72c9-4f69-bded-556caf15cc62).