vecchia_grouped_meanzero_loglik: Grouped Vecchia approximation to the Gaussian loglikelihood,...

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

Description

This function returns a grouped version (Guinness, 2018) of 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_grouped_meanzero_loglik(covparms, covfun_name, y, locs, NNlist)

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.
NNlist	A neighbor list object, the output from group_obs.

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)
NNlist <- group_obs(NNarray)
#loglik <- vecchia_grouped_meanzero_loglik( covparms, "matern_isotropic", y, locs, NNlist )

GpGp documentation built on June 10, 2021, 1:07 a.m.