Introduction

GaussianProcesses

Introduction

Gaussian processes are a family of stochastic processes which provide a flexible nonparametric tool for modelling data. A Gaussian Process places a prior over functions, and can be described as an infinite dimensional generalisation of a multivariate Normal distribution. Moreover, the joint distribution of any finite collection of points is a multivariate Normal. This process can be fully characterised by its mean and covariance functions, where the mean of any point in the process is described by the mean function and the covariance between any two observations is specified by the kernel. Given a set of observed real-valued points over a space, the Gaussian Process is used to make inference on the values at the remaining points in the space.

For an extensive review of Gaussian Processes there is an excellent book Gaussian Processes for Machine Learning by Rasmussen and Williams, (2006)

Installation

GaussianProcesses.jl requires Julia version 0.5 or above. To install GaussianProcesses.jl run the following command inside the Julia package REPL

pkg> add GaussianProcesses

or in the standard REPL

julia> using Pkg
julia> Pkg.add("GaussianProcesses")