dmnorm_sgv: Function for the evaluating the SGV approximate density.

In BayesNSGP: Bayesian Analysis of Non-Stationary Gaussian Process Models

Description

dmnorm_sgv (and rmnorm_sgv) calculate the approximate SGV likelihood for a fixed set of parameters (i.e., the U matrix). Finally, the distributions must be registered within nimble.

Usage

dmnorm_sgv(x, mean, U, N, k, log = 1)

Arguments

x	Vector of measurements
mean	Vector of mean valiues
U	Matrix of size N x 3; representation of a sparse N x N Cholesky of the precision matrix. The first two columns contain row and column indices, respectively, and the last column is the nonzero elements of the matrix.
N	Number of measurements in x
k	Number of neighbors for the SGV approximation.
log	Logical; should the density be evaluated on the log scale.

Value

Returns the SGV approximation to the Gaussian likelihood.

BayesNSGP documentation built on Jan. 9, 2022, 9:07 a.m.