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dc.contributor.authorLitvinenko, Alexander
dc.date.accessioned2017-05-23T06:17:42Z
dc.date.available2017-05-23T06:17:42Z
dc.date.issued2016-06-23
dc.identifier.urihttp://hdl.handle.net/10754/623696
dc.description.abstractWe develop a non-linear approximation of expensive Bayesian formula. This non-linear approximation is applied directly to Polynomial Chaos Coefficients. In this way, we avoid Monte Carlo sampling and sampling error. We can show that the famous Kalman Update formula is a particular case of this update.
dc.description.sponsorshipSRI UQ, ECRC KAUST
dc.subjectLorenz 84
dc.subjectBayesian Update
dc.subjectPCE
dc.subjectBayesian surrogate
dc.subjectupdate of PCE coefficients
dc.subjectposterior
dc.titleNon-Linear Approximation of Bayesian Update
dc.typePresentation
dc.contributor.departmentCenter for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)
dc.conference.dateJune 2016
dc.conference.nameECRC Meeting
dc.conference.locationKAUST
refterms.dateFOA2018-06-13T16:55:04Z


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We derive non-linear Bayesian update surrogate and apply it direct to PCE coefficients

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