calculateAD_ns: Calculate A and D matrices for the NNGP approximation

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

Description

calculateAD_ns calculates A and D matrices (the Cholesky of the precision matrix) needed for the NNGP approximation.

Usage

calculateAD_ns(
  dist1_3d,
  dist2_3d,
  dist12_3d,
  Sigma11,
  Sigma22,
  Sigma12,
  log_sigma_vec,
  log_tau_vec,
  nID,
  N,
  k,
  nu,
  d
)

Arguments

dist1_3d	N x (k+1) x (k+1) array of distances in the x-coordinate direction.
dist2_3d	N x (k+1) x (k+1) array of distances in the y-coordinate direction.
dist12_3d	N x (k+1) x (k+1) array of cross-distances.
Sigma11	N-vector; 1-1 element of the Sigma() process.
Sigma22	N-vector; 2-2 element of the Sigma() process.
Sigma12	N-vector; 1-2 element of the Sigma() process.
log_sigma_vec	N-vector; process standard deviation values.
log_tau_vec	N-vector; nugget standard deviation values.
nID	N x k matrix of neighbor indices.
N	Scalar; number of data measurements.
k	Scalar; number of nearest neighbors.
nu	Scalar; Matern smoothness parameter.
d	Scalar; dimension of the spatial domain.

Value

A N x (k+1) matrix; the first k columns are the 'A' matrix, and the last column is the 'D' vector.

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