gp_lml_deriv: Compute the derivative of the log marginal likelihood

In ebenmichael/gaussianProcess: Fits Gaussian Processes for Regression

Description

Compute the derivative of the log marginal likelihood

Usage

gp_lml_deriv(hyper.params, X, y, kernel.type, order = 5/2, noise.var = 1)

Arguments

hyper.params	Hyper parameters as a vector (for rbf c(amplitude, scales))
X	An n x d matrix of covariates for observed data
y	An n dimensional vector of outputs for observed data
kernel.type	The kernel function to use
order	The order of the kernel, defaults to 5/2
noise.var	The variance of the noise around the function

Value

The gradient of the log marginal likelihood at hyper.params

ebenmichael/gaussianProcess documentation built on May 15, 2019, 7:30 p.m.