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dc.contributor.authorLitvinenko, Alexander
dc.contributor.authorGenton, Marc G.
dc.contributor.authorSun, Ying
dc.contributor.authorTempone, Raul
dc.date.accessioned2017-06-05T08:35:46Z
dc.date.available2017-06-05T08:35:46Z
dc.date.issued2015-01-07
dc.identifier.urihttp://hdl.handle.net/10754/624057
dc.description.abstractWe approximate large non-structured covariance matrices in the H-matrix format with a log-linear computational cost and storage O(n log n). 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 optimal design
dc.titleHierarchical matrix approximation of large covariance matrices
dc.typePoster
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences & Engineering (CEMSE)
dc.conference.dateJanuary 6-9, 2015
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)
dc.conference.locationKAUST
kaust.personLitvinenko, Alexander
kaust.personGenton, Marc G.
kaust.personSun, Ying
kaust.personTempone, Raul
refterms.dateFOA2018-06-14T04:33:33Z


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