Structures and algorithms for post-processing large data sets and multi-variate functions in spatio-temporal statistics

Handle URI:
http://hdl.handle.net/10754/626345
Title:
Structures and algorithms for post-processing large data sets and multi-variate functions in spatio-temporal statistics
Authors:
Litvinenko, Alexander ( 0000-0001-5427-3598 )
Abstract:
Matrices began in the 2nd century BC with the Chinese. One can find traces, which go to the 4th century BC to the Babylonians. The text ``Nine Chapters of the Mathematical Art'' written during the Han Dynasty in China gave the first known example of matrix methods. They were used to solve simultaneous linear equations (more in http://math.nie.edu.sg/bwjyeo/it/MathsOnline_AM/livemath/the/IT3AMMatricesHistory.html). The first ideas of the maximum likelihood estimation (MLE) was introduces by Laplace (1749-1827), by Gauss (1777-1855), the Likelihood was defined by Thiele Thorvald (1838-1910). Why we still use matrices? The matrix data format is more than 2200 years old. Our world is multi-dimensional! Why not to introduce a more appropriate data format and why not to reformulate the MLE method for it? In this work we are utilizing the low-rank tensor formats for multi-dimansional functions, which appear in spatial statistics.
KAUST Department:
Extreme Computing Research Center
Conference/Event name:
ECRC internal talk
Issue Date:
10-Dec-2017
Type:
Presentation
Sponsors:
KAUST
Appears in Collections:
Presentations

Full metadata record

DC FieldValue Language
dc.contributor.authorLitvinenko, Alexanderen
dc.date.accessioned2017-12-11T06:28:27Z-
dc.date.available2017-12-11T06:28:27Z-
dc.date.issued2017-12-10-
dc.identifier.urihttp://hdl.handle.net/10754/626345-
dc.description.abstractMatrices began in the 2nd century BC with the Chinese. One can find traces, which go to the 4th century BC to the Babylonians. The text ``Nine Chapters of the Mathematical Art'' written during the Han Dynasty in China gave the first known example of matrix methods. They were used to solve simultaneous linear equations (more in http://math.nie.edu.sg/bwjyeo/it/MathsOnline_AM/livemath/the/IT3AMMatricesHistory.html). The first ideas of the maximum likelihood estimation (MLE) was introduces by Laplace (1749-1827), by Gauss (1777-1855), the Likelihood was defined by Thiele Thorvald (1838-1910). Why we still use matrices? The matrix data format is more than 2200 years old. Our world is multi-dimensional! Why not to introduce a more appropriate data format and why not to reformulate the MLE method for it? In this work we are utilizing the low-rank tensor formats for multi-dimansional functions, which appear in spatial statistics.en
dc.description.sponsorshipKAUSTen
dc.subjectMLEen
dc.subjectlog-likelihooden
dc.subjectTucker tensoren
dc.subjectlow-rank tensor approximationen
dc.subjectFourier transformen
dc.subjectLaplace transformen
dc.subjectsinc quadratureen
dc.subjectKriging in low-rank formaten
dc.titleStructures and algorithms for post-processing large data sets and multi-variate functions in spatio-temporal statisticsen
dc.typePresentationen
dc.contributor.departmentExtreme Computing Research Centeren
dc.conference.date10.12.2017en
dc.conference.nameECRC internal talken
dc.conference.locationKAUSTen
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