exponential_isotropic: Isotropic exponential covariance function

In joeguinness/GpGp: Fast Gaussian Process Computation Using Vecchia's Approximation

Isotropic exponential covariance function

Description

From a matrix of locations and covariance parameters of the form (variance, range, nugget), return the square matrix of all pairwise covariances.

Usage

exponential_isotropic(covparms, locs)

d_exponential_isotropic(covparms, locs)

d_matern15_isotropic(covparms, locs)

d_matern25_isotropic(covparms, locs)

Arguments

covparms	A vector with covariance parameters in the form (variance, range, nugget)
locs	A matrix with n rows and d columns. Each row of locs is a point in R^d.

Value

A matrix with n rows and n columns, with the i,j entry containing the covariance between observations at locs[i,] and locs[j,].

Functions

• d_exponential_isotropic(): Derivatives of isotropic exponential covariance

• d_matern15_isotropic(): Derivatives of isotropic matern covariance with smoothness 1.5

• d_matern25_isotropic(): Derivatives of isotropic matern covariance function with smoothness 2.5

Parameterization

The covariance parameter vector is (variance, range, nugget) = (\sigma^2,\alpha,\tau^2), and the covariance function is parameterized as

M(x,y) = \sigma^2 exp( - || x - y ||/ \alpha )

The nugget value \sigma^2 \tau^2 is added to the diagonal of the covariance matrix. NOTE: the nugget is \sigma^2 \tau^2 , not \tau^2 .

joeguinness/GpGp documentation built on Feb. 22, 2024, 9:43 a.m.