Linv_t_mult: Multiply transpose of approximate inverse Cholesky by a...
In GpGp: Fast Gaussian Process Computation Using Vecchia's Approximation

Description Usage Arguments Value Examples

Vecchia's approximation implies a sparse approximation to the inverse Cholesky factor of the covariance matrix. This function returns the result of multiplying the transpose of that matrix by a vector.

1	Linv_t_mult(Linv, z, NNarray)

`Linv`	Entries of the sparse inverse Cholesky factor, usually the output from `vecchia_Linv`.
`z`	the vector to be multiplied
`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`.

the product of the transpose of the sparse inverse Cholesky factor with a vector

n <- 2000
locs <- matrix( runif(2*n), n, 2 )
covparms <- c(2, 0.2, 0.75, 0.1)
NNarray <- find_ordered_nn(locs,20)
Linv <- vecchia_Linv( covparms, "matern_isotropic", locs, NNarray )
z1 <- rnorm(n)
z2 <- Linv_t_mult(Linv, z1, NNarray)