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dc.contributor.authorLitvinenko, Alexander
dc.contributor.authorGenton, Marc G.
dc.contributor.authorSun, Ying
dc.contributor.authorKeyes, David E.
dc.date.accessioned2017-05-31T11:23:14Z
dc.date.available2017-05-31T11:23:14Z
dc.date.issued2016-10-25
dc.identifier.citationLitvinenko A, Genton M, Sun Y, Keyes D (2016) ℋ-matrix techniques for approximating large covariance matrices and estimating its parameters. PAMM 16: 731–732. Available: http://dx.doi.org/10.1002/pamm.201610354.
dc.identifier.issn1617-7061
dc.identifier.doi10.1002/pamm.201610354
dc.identifier.urihttp://hdl.handle.net/10754/623937
dc.description.abstractIn this work the task is to use the available measurements to estimate unknown hyper-parameters (variance, smoothness parameter and covariance length) of the covariance function. We do it by maximizing the joint log-likelihood function. This is a non-convex and non-linear problem. To overcome cubic complexity in linear algebra, we approximate the discretised covariance function in the hierarchical (ℋ-) matrix format. The ℋ-matrix format has a log-linear computational cost and storage O(knlogn), where rank k is a small integer. On each iteration step of the optimization procedure the covariance matrix itself, its determinant and its Cholesky decomposition are recomputed within ℋ-matrix format. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)
dc.description.sponsorshipAlexander Litvinenko and his research work reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST), SRI UQ and ECRC Centers.
dc.publisherWiley
dc.relation.urlhttp://onlinelibrary.wiley.com/doi/10.1002/pamm.201610354/abstract
dc.titleℋ-matrix techniques for approximating large covariance matrices and estimating its parameters
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentExtreme Computing Research Center
dc.contributor.departmentStatistics Program
dc.identifier.journalPAMM
kaust.personLitvinenko, Alexander
kaust.personGenton, Marc G.
kaust.personSun, Ying
kaust.personKeyes, David E.
dc.date.published-online2016-10-25
dc.date.published-print2016-10


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