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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.
vecchia_meanzero_loglik(covparms, covfun_name, y, locs, NNarray)a list containing
loglik: the loglikelihood
A vector of covariance parameters appropriate for the specified covariance function
See GpGp for information about covariance
functions.
vector of response values
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.
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.
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 )
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