Show simple item record

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.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
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


Files in this item

This item appears in the following Collection(s)

Show simple item record