exponential_spacetime: Spatial-Temporal 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, range_1, range_2, nugget), return the square matrix of all pairwise covariances.

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exponential_spacetime(covparms, locs)

d_exponential_spacetime(covparms, locs)

`covparms`	A vector with covariance parameters in the form (variance, range_1, range_2, nugget). range_1 is the spatial range, and range_2 is the temporal range.
`locs`	A matrix with `n` rows and `d+1` columns. Each row of locs is a point in R^d+1. The first `d` columns should contain the spatial coordinates. The last column contains the times.

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_spacetime: Derivatives with respect to parameters

The covariance parameter vector is (variance, range_1, range_2, nugget). The covariance function is parameterized as

M(x,y) = σ^2 exp( - || D^{-1}(x - y) || )

where D is a diagonal matrix with (range_1, ..., range_1, range_2) on the diagonals. 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.