1.
MethodsX
; 7: 100600, 2020.
Artigo
em Inglês
| MEDLINE
| ID: mdl-32021810
RESUMO
We provide more technical details about the HLIBCov package, which is using parallel hierarchical (H-) matrices to: â¢Approximate large dense inhomogeneous covariance matrices with a log-linear computational cost and storage requirement.â¢Compute matrix-vector product, Cholesky factorization and inverse with a log-linear complexity.â¢Identify unknown parameters of the covariance function (variance, smoothness, and covariance length). These unknown parameters are estimated by maximizing the joint Gaussian log-likelihood function. To demonstrate the numerical performance, we identify three unknown parameters in an example with 2,000,000 locations on a PC-desktop.