exponential_anisotropic2D: Geometrically anisotropic exponential covariance function...

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

Geometrically anisotropic exponential covariance function (two dimensions)

Description

From a matrix of locations and covariance parameters of the form (variance, L11, L21, L22, nugget), return the square matrix of all pairwise covariances.

Usage

exponential_anisotropic2D(covparms, locs)

d_exponential_anisotropic2D(covparms, locs)

Arguments

covparms	A vector with covariance parameters in the form (variance, L11, L21, L22, nugget)
locs	A matrix with n rows and 2 columns. Each row of locs is a point in R^2.

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_anisotropic2D(): Derivatives of anisotropic exponential covariance

Parameterization

The covariance parameter vector is (variance, L11, L21, L22, nugget) where L11, L21, L22, are the three non-zero entries of a lower-triangular matrix L. The covariances are

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

This means that L11 is interpreted as an inverse range parameter in the first dimension. 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.