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dc.contributor.authorLitvinenko, Alexander
dc.contributor.authorGenton, Marc G.
dc.contributor.authorSun, Ying
dc.contributor.authorTempone, Raul
dc.date.accessioned2017-05-16T08:19:36Z
dc.date.available2017-05-16T08:19:36Z
dc.date.issued2015-01-05
dc.identifier.urihttp://hdl.handle.net/10754/623621
dc.description.abstractWe approximate large non-structured covariance matrices in the H-matrix format with a log-linear computational cost and storage O(nlogn). We compute inverse, Cholesky decomposition and determinant in H-format. As an example we consider the class of Matern covariance functions, which are very popular in spatial statistics, geostatistics, machine learning and image analysis. Applications are: kriging and op- timal design.
dc.description.sponsorshipSRI UQ, KAUST
dc.relation.urlhttps://sri-uq.kaust.edu.sa/Pages/UQAnnualWorkshop2015.aspx
dc.subjecthierarchical matrices
dc.subjectapproximate covariance
dc.subjectKriging
dc.subjectgeostatistical optimal design
dc.subjectweather forecasting
dc.titleHierarchical matrix approximation of large covariance matrices
dc.typePoster
dc.contributor.departmentCenter for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)
dc.contributor.departmentSpatio-Temporal Statistics and Data Analysis Group
dc.contributor.departmentEnvironmental Statistics Group, KAUST
dc.conference.dateJanuary 2015
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)
dc.conference.locationKAUST, B1
refterms.dateFOA2018-06-13T15:29:24Z


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Application of hierarchical matrices for approximating large covariance matrices, kriging, geostatistical optimal design

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