GPLinearODE (GLO) struct

GLO{T1<:Real, T2<:Integer} a.k.a. GPLinearODE

A structure that holds all of the relevant information for constructing the GLOM model

Arguments

• kernel::Function: kernel function for the latent Gaussian process (GP)
• n_kern_hyper::T2: amount of hyperparameters for the kernel function
• n_dif::T2: amount of times you are differenting the base kernel
• n_out::T2: amount of outputs you are jointly modelling
• x_obs::Vector{T1}: the observation times/phases
• y_obs::Vector{T1}: the flattened, observed data
• noise::Vector{T1}: the measurement noise at all observations (σ, not σ^2)
• normals::Vector{T1}: the normalization of each section of y_obs
• a::Matrix{T1}: the meta kernel coefficients
• non_zero_hyper_inds::Vector{T2}: the indices of the non-zero hyperparameters
• coeff_orders::AbstractArray{T2,6}: The powers that each a coefficient is taken to for each part of the covariance matrix construction. Used for constructing differentiated versions of the kernel
• coeff_coeffs::AbstractArray{T2,4}: The coefficients that each a coefficient is multiplied by for each part of the covariance matrix construction. Used for constructing differentiated versions of the kernel
• covariance::Array{T1, 3}: the measurement covariance at all observations
• has_covariance::Bool: Whether or not there are measurement covariances (as opposed to just having measurement noises)
• kernel_changes_with_output::Bool: A flag for if the kernel is not the same for each output (e.g. there is a time delay kernel hyperparameter that changes)

normalize_GLO!(glo)

Standardize the outputs in glo.y_obs (and glo.noise) to have 0-mean and unit variance and store the normalization in glo.normals

normalize_GLO!(glo, renorms)

Divide the outputs in glo.y_obs and glo.noise and multiply glo.normals by renorms