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

Approximations for Gaussian processes: sparse variational inducing point approximations, Laplace approximation, ...
https://github.com/juliagaussianprocesses/approximategps.jl

Last synced: 12 months ago
JSON representation

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

• Host: GitHub
• URL: https://github.com/juliagaussianprocesses/approximategps.jl
• Owner: JuliaGaussianProcesses
• License: other
• Created: 2021-06-19T16:32:31.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2024-07-14T16:24:56.000Z (almost 2 years ago)
• Last Synced: 2025-06-30T10:09:24.743Z (about 1 year ago)
• Language: Julia
• Homepage: https://juliagaussianprocesses.github.io/ApproximateGPs.jl/dev
• Size: 30.5 MB
• Stars: 38
• Watchers: 6
• Forks: 7
• Open Issues: 25
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# ApproximateGPs

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

[![CI](https://github.com/JuliaGaussianProcesses/ApproximateGPs.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/JuliaGaussianProcesses/ApproximateGPs.jl/actions/workflows/CI.yml)

[![Codecov](https://codecov.io/gh/JuliaGaussianProcesses/ApproximateGPs.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/JuliaGaussianProcesses/ApproximateGPs.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)

## Aim of this package

Provide various algorithms for approximate inference in latent Gaussian process models, currently focussing on non-conjugate (non-Gaussian) likelihoods and sparse approximations.

## Structure

Each approximation lives in its own submodule (`Module`), though

in general using the exported API is sufficient.

The main API is:

* `posterior(approximation, lfx::LatentFiniteGP, ys)` to obtain the posterior

  approximation to `lfx` conditioned on the observations `ys`.

* `approx_lml(approximation, lfx::LatentFiniteGP, ys)` which returns the

  marginal likelihood approximation that can be used for hyperparameter

  optimisation.

Currently implemented approximations:

* `LaplaceApproximation`

* `SparseVariationalApproximation`

  NOTE: requires optimisation of the variational distribution even for fixed

  hyperparameters.