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

The Mat\'ern covariance function is a popular choice for modeling dependencein spatial environmental data. Standard Mat\'ern covariance models are,however, often computationally infeasible for large data sets. In this work,recent results for Markov approximations of Gaussian Mat\'{e}rn fields based onHilbert space approximations are extended using wavelet basis functions. TheseMarkov approximations are compared with two of the most popular methods forefficient covariance approximations; covariance tapering and the processconvolution method. The results show that, for a given computational cost, theMarkov methods have a substantial gain in accuracy compared with the othermethods.

How do Markov approximations compare with other methods for large spatial data sets?