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Gaussian processes are a class of flexible nonparametric Bayesian tools thatare widely used across the sciences, and in industry, to model complex datasources. Key to applying Gaussian process models is the availability ofwell-developed open source software, which is available in many programminglanguages. In this paper, we present a tutorial of the GaussianProcesses.jlpackage that has been developed for the Julia programming language.GaussianProcesses.jl utilises the inherent computational benefits of the Julialanguage, including multiple dispatch and just-in-time compilation, to producea fast, flexible and user-friendly Gaussian processes package. The packageprovides many mean and kernel functions with supporting inference tools to fitexact Gaussian process models, as well as a range of alternative likelihoodfunctions to handle non-Gaussian data (e.g. binary classification models) andsparse approximations for scalable Gaussian processes. The package makesefficient use of existing Julia packages to provide users with a range ofoptimization and plotting tools.

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