Show simple item record

dc.contributor.authorLitvinenko, Alexander
dc.date.accessioned2018-03-12T07:58:17Z
dc.date.available2018-03-12T07:58:17Z
dc.date.issued2018-03-12
dc.identifier.urihttp://hdl.handle.net/10754/627299
dc.description.abstractPart 1: Parallel H-matrices in spatial statistics 1. Motivation: improve statistical model 2. Tools: Hierarchical matrices 3. Matern covariance function and joint Gaussian likelihood 4. Identification of unknown parameters via maximizing Gaussian log-likelihood 5. Implementation with HLIBPro. Part 2: Low-rank Tucker tensor methods in spatial statistics
dc.description.sponsorshipKAUST
dc.relation.urlhttps://www.siam.org/meetings/pp18/
dc.subjectLow-rank Tucker tensor
dc.subjectParameter identification
dc.subjectHLIBpro
dc.subjectHLIB
dc.subjectMatern covariance
dc.subjectSpatial statistics
dc.titleApplication of Parallel Hierarchical Matrices and Low-Rank Tensors in Spatial Statistics and Parameter Identification
dc.typePresentation
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.nameSIAM Conference on Parallel Processing for Scientific Computing
dc.conference.locationApril 7-11, 2018
refterms.dateFOA2018-06-14T03:42:19Z


Files in this item

Thumbnail
Name:
talk_litvinenko_SIAMPP.pdf
Size:
2.705Mb
Format:
PDF
Description:
My talk, presented on SIAM PP 2018 Conference

This item appears in the following Collection(s)

Show simple item record