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GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language. / Fairbrother, Jamie; Nemeth, Christopher; Pinder, Thomas et al.
In: Journal of Statistical Software, Vol. 102, No. 1, 30.04.2022, p. 1-36.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Fairbrother, J, Nemeth, C, Pinder, T, Rischard, M & Brea, J 2022, 'GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language', Journal of Statistical Software, vol. 102, no. 1, pp. 1-36. https://doi.org/10.18637/jss.v102.i01

APA

Fairbrother, J., Nemeth, C., Pinder, T., Rischard, M., & Brea, J. (2022). GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language. Journal of Statistical Software, 102(1), 1-36. https://doi.org/10.18637/jss.v102.i01

Vancouver

Fairbrother J, Nemeth C, Pinder T, Rischard M, Brea J. GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language. Journal of Statistical Software. 2022 Apr 30;102(1):1-36. doi: 10.18637/jss.v102.i01

Author

Fairbrother, Jamie ; Nemeth, Christopher ; Pinder, Thomas et al. / GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language. In: Journal of Statistical Software. 2022 ; Vol. 102, No. 1. pp. 1-36.

Bibtex

@article{e89c04f8ea2c4235a452231d75ac6b83,
title = "GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language",
abstract = "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 utilises 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 tools.",
keywords = "Gaussian process, nonparametric Bayesian methods, regression, classification, Julia",
author = "Jamie Fairbrother and Christopher Nemeth and Thomas Pinder and Maxime Rischard and Johanni Brea",
year = "2022",
month = apr,
day = "30",
doi = "10.18637/jss.v102.i01",
language = "English",
volume = "102",
pages = "1--36",
journal = "Journal of Statistical Software",
issn = "1548-7660",
publisher = "University of California at Los Angeles",
number = "1",

}

RIS

TY - JOUR

T1 - GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language

AU - Fairbrother, Jamie

AU - Nemeth, Christopher

AU - Pinder, Thomas

AU - Rischard, Maxime

AU - Brea, Johanni

PY - 2022/4/30

Y1 - 2022/4/30

N2 - 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 utilises 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 tools.

AB - 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 utilises 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 tools.

KW - Gaussian process

KW - nonparametric Bayesian methods

KW - regression

KW - classification

KW - Julia

U2 - 10.18637/jss.v102.i01

DO - 10.18637/jss.v102.i01

M3 - Journal article

VL - 102

SP - 1

EP - 36

JO - Journal of Statistical Software

JF - Journal of Statistical Software

SN - 1548-7660

IS - 1

ER -