EasyGPs.jl

EasyGPs.jl is a package that defines a high-level API for the JuliaGaussianProcesses ecosystem. It is aimed at people who want to use GPs to do exploratory analysis, model data and make predictions without having to deal with all the low-level detail.

Usage

In order to fit a GP, define one according to the familiar AbstractGP.jl interface and let EasyGPs.jl handle the rest. The entry point for this is EasyGPs.fit (not exported):

using EasyGPs

kernel = 1.0 * with_lengthscale(SEKernel(), 1.0)
gp = with_gaussian_noise(GP(0.0, kernel), 0.1)
x = 0:0.1:10
y = sin.(x) .+ 0.1 .* randn(length(x))
fitted_gp = EasyGPs.fit(gp, x, y)

Under the hood, this will recognize the parameters (mean, variance, lengthscale) of the GP you defined and automatically construct a parameterized model. It will then choose a cost function, optimizer, and AD backend, and determine the optimal parameters.