GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language
Dublin Core
Title
GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language
Subject
: Gaussian processes, nonparametric Bayesian methods, regression, classification,
Julia.
Julia.
Description
Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely
used across the sciences, and in industry, to model complex data sources. Key to applying
Gaussian process models is the availability of well-developed open source software, which
is available in many programming languages. In this paper, we present a tutorial of
the GaussianProcesses.jl package that has been developed for the Julia programming
language. GaussianProcesses.jl utilizes the inherent computational benefits of the Julia
language, including multiple dispatch and just-in-time compilation, to produce a fast,
flexible and user-friendly Gaussian processes package. The package provides many mean
and kernel functions with supporting inference tools to fit exact Gaussian process models,
as well as a range of alternative likelihood functions to handle non-Gaussian data (e.g.,
binary classification models) and sparse approximations for scalable Gaussian processes.
The package makes efficient use of existing Julia packages to provide users with a range
of optimization and plotting tool
used across the sciences, and in industry, to model complex data sources. Key to applying
Gaussian process models is the availability of well-developed open source software, which
is available in many programming languages. In this paper, we present a tutorial of
the GaussianProcesses.jl package that has been developed for the Julia programming
language. GaussianProcesses.jl utilizes the inherent computational benefits of the Julia
language, including multiple dispatch and just-in-time compilation, to produce a fast,
flexible and user-friendly Gaussian processes package. The package provides many mean
and kernel functions with supporting inference tools to fit exact Gaussian process models,
as well as a range of alternative likelihood functions to handle non-Gaussian data (e.g.,
binary classification models) and sparse approximations for scalable Gaussian processes.
The package makes efficient use of existing Julia packages to provide users with a range
of optimization and plotting tool
Creator
Jamie Fairbrother
Source
https://www.jstatsoft.org/article/view/v102i01
Publisher
https://www.jstatsoft.org/article/view/v102i01
Date
April 2022
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Jamie Fairbrother, “GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8247.