exponential_anisotropic3D: Geometrically anisotropic exponential covariance function...
In GpGp: Fast Gaussian Process Computation Using Vecchia's Approximation

Description Usage Arguments Value Functions Parameterization

From a matrix of locations and covariance parameters of the form (variance, L11, L21, L22, L31, L32, L33, nugget), return the square matrix of all pairwise covariances.

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exponential_anisotropic3D(covparms, locs)

d_exponential_anisotropic3D(covparms, locs)

`covparms`	A vector with covariance parameters in the form (variance, L11, L21, L22, L31, L32, L33, nugget)
`locs`	A matrix with `n` rows and `3` columns. Each row of locs is a point in R^3.

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

d_exponential_anisotropic3D: Derivatives of anisotropic exponential covariance

The covariance parameter vector is (variance, L11, L21, L22, L31, L32, L33, nugget) where L11, L21, L22, L31, L32, L33 are the six non-zero entries of a lower-triangular matrix L. The covariances are

M(x,y) = σ^2 exp(-|| L x - L y || )

This means that L11 is interpreted as an inverse range parameter in the first dimension. The nugget value σ^2 τ^2 is added to the diagonal of the covariance matrix. NOTE: the nugget is σ^2 τ^2 , not τ^2 .

GpGp documentation built on June 10, 2021, 1:07 a.m.