I am a statistics PhD student at Lancaster University, focussing on developing more scalable Gaussian processes in order to model the genetic and environmental effects of air quality. Prior to this, I studied an MSc in Data Science at Lancaster University, and before that a BSc in Mathematics and Statistics at the University of Reading. Before undertaking a PhD, I worked as an NLP Data Scientist at Relative Insight, researching and developing an abstractive text summarisation tool. To learn a little more about me, click here.
PhD in Statistics, 2018-Present
MSc in Data Science, 2018
BSc in Mathematics & Statistics, 2017
University of Reading
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.