Home > Research > Publications & Outputs > GaussianProcesses.jl

Electronic data

  • 1812.09064v2

    Final published version, 2.72 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Keywords

View graph of relations

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

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>21/12/2018
<mark>Journal</mark>arXiv
Publication StatusPublished
<mark>Original language</mark>English

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.

Bibliographic note

32 pages, 10 figures. Updated version includes sparse GPs