cov_joint: Covariance for joint distribution

In mcgf: Markov Chain Gaussian Fields Simulation and Parameter Estimation

Covariance for joint distribution

Description

Covariance for joint distribution

Usage

cov_joint(cov)

cov_par(cov, horizon = 1, n_var, joint = FALSE)

Arguments

cov	Array of covariance matrices.
horizon	Forecast horizon, default is 1.
n_var	Number of locations.
joint	Logical; True if cov is the joint covariance matrix.

Details

The covariance matrix of the joint distribution has the block toeplitz structure. Input cov is assumed to be an array of cross-covariance matrices where the ith matrix slice correspond to the (i-1)th time lag. For example, cov[, , 1] is the cross-covariance matrix for time lag 0. All matrices in cov are used to construct the joint covariance matrix.

cov_par gives weights and covariance matrix for the current values..

Value

The joint covariance matrix for the joint distribution of the current values and the past values for a Markov chain Gaussian field.

mcgf documentation built on June 29, 2024, 9:09 a.m.