English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We use available measurements to estimate the unknown parameters (variance,smoothness parameter, and covariance length) of a covariance function bymaximizing the joint Gaussian log-likelihood function. To overcome cubiccomplexity in the linear algebra, we approximate the discretized covariancefunction in the hierarchical (H-) matrix format. The H-matrix format has alog-linear computational cost and storage $O(kn \log n)$, where the rank $k$ isa small integer and $n$ is the number of locations. The H-matrix techniqueallows us to work with general covariance matrices in an efficient way, sinceH-matrices can approximate inhomogeneous covariance functions, with a fairlygeneral mesh that is not necessarily axes-parallel, and neither the covariancematrix itself nor its inverse have to be sparse. We demonstrate our method withMonte Carlo simulations and an application to soil moisture data. The C, C++codes and data are freely available.

Likelihood Approximation With Hierarchical Matrices For Large Spatial Datasets