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dc.contributor.authorLitvinenko, Alexander
dc.date.accessioned2018-03-05T12:17:14Z
dc.date.available2018-03-05T12:17:14Z
dc.date.issued2018-03-01
dc.identifier.urihttp://hdl.handle.net/10754/627222
dc.description.abstract1. Introduction to UQ 2. Low-rank tensors for representation of big/high-dimensional data 3. Inverse Problem via Bayesian Update 4. R-INLA and advance numerics for spatio-temporal statistics 5. High Performance Computing, parallel algorithms
dc.subjectLow-rank tensors
dc.subjectUncertainty Quantification
dc.subjectParallel Computing
dc.subjectH-matrices
dc.subjectBayesian surrogate
dc.titleUncertainty Quantification - an Overview
dc.typePresentation
dc.contributor.departmentBayesian computational statistics, CEMSE
dc.conference.date1 March, 2018
dc.conference.nameinvited talk, Durham University, UK
dc.conference.locationDurham University, UK
refterms.dateFOA2018-06-13T11:58:36Z


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