vecchia_meanzero_loglik: Vecchia's approximation to the Gaussian loglikelihood, zero...

In joeguinness/GpGp: Fast Gaussian Process Computation Using Vecchia's Approximation

Vecchia's approximation to the Gaussian loglikelihood, zero mean

Description

This function returns Vecchia's (1988) approximation to the Gaussian loglikelihood. The approximation modifies the ordered conditional specification of the joint density; rather than each term in the product conditioning on all previous observations, each term conditions on a small subset of previous observations.

Usage

vecchia_meanzero_loglik(covparms, covfun_name, y, locs, NNarray)

Arguments

covparms	A vector of covariance parameters appropriate for the specified covariance function
covfun_name	See GpGp for information about covariance functions.
y	vector of response values
locs	matrix of locations. Row i of locs specifies the location of element i of y, and so the length of y should equal the number of rows of locs.
NNarray	A matrix of indices, usually the output from find_ordered_nn. Row i contains the indices of the observations that observation i conditions on. By convention, the first element of row i is i.

Value

a list containing

• loglik: the loglikelihood

Examples

n1 <- 20
n2 <- 20
n <- n1*n2
locs <- as.matrix( expand.grid( (1:n1)/n1, (1:n2)/n2 ) )
covparms <- c(2, 0.2, 0.75, 0)
y <- fast_Gp_sim(covparms, "matern_isotropic", locs, 50 )
ord <- order_maxmin(locs)
NNarray <- find_ordered_nn(locs,20)
#loglik <- vecchia_meanzero_loglik( covparms, "matern_isotropic", y, locs, NNarray )

joeguinness/GpGp documentation built on Feb. 22, 2024, 9:43 a.m.