Non-Linear Approximation of Bayesian Update

dc.conference.dateJune 2016
dc.conference.locationKAUST
dc.conference.nameECRC Meeting
dc.contributor.authorLitvinenko, Alexander
dc.contributor.departmentCenter for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)
dc.date.accessioned2017-05-23T06:17:42Z
dc.date.available2017-05-23T06:17:42Z
dc.date.issued2016-06-23
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.identifier.urihttp://hdl.handle.net/10754/623696
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
display.details.left<span><h5>Type</h5>Presentation<br><br><h5>Authors</h5><a href="https://repository.kaust.edu.sa/search?query=orcid.id:0000-0001-5427-3598&spc.sf=dc.date.issued&spc.sd=DESC">Litvinenko, Alexander</a> <a href="https://orcid.org/0000-0001-5427-3598" target="_blank"><img src="https://repository.kaust.edu.sa/server/api/core/bitstreams/82a625b4-ed4b-40c8-865a-d6a5225a26a4/content" width="16" height="16"/></a><br><br><h5>KAUST Department</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Center for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ),equals">Center for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)</a><br><br><h5>Date</h5>2016-06-23</span>
display.details.right<span><h5>Abstract</h5>We 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.<br><br><h5>Acknowledgements</h5>SRI UQ, ECRC KAUST<br><br><h5>Conference/Event Name</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.conference=ECRC Meeting,equals">ECRC Meeting</a></span>
orcid.authorLitvinenko, Alexander::0000-0001-5427-3598
orcid.id0000-0001-5427-3598
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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