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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Iterative methods for fitting a Gaussian Random Field (GRF) model via maximumlikelihood (ML) estimation requires solving a nonconvex optimization problem.The problem is aggravated for anisotropic GRFs where the number of covariancefunction parameters increases with the dimension. Even evaluation of thelikelihood function requires $O(n^3)$ floating point operations, where $n$denotes the number of data locations. In this paper, we propose a new two-stageprocedure to estimate the parameters of second-order stationary GRFs. First, aconvex likelihood problem regularized with a weighted $\ell_1$-norm, utilizingthe available distance information between observation locations, is solved tofit a sparse precision (inverse covariance) matrix to the observed data.Second, the parameters of the covariance function are estimated by solving aleast squares problem. Theoretical error bounds for the solutions of stage Iand II problems are provided, and their tightness are investigated.

On the Theoretical Guarantees for Parameter Estimation of Gaussian Random Field Models: A Sparse Precision Matrix Approach