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dc.contributor.authorMatthies, Hermann G.
dc.contributor.authorLitvinenko, Alexander
dc.contributor.authorRosic, Bojana V.
dc.contributor.authorZander, Elmar
dc.date.accessioned2017-12-28T07:32:11Z
dc.date.available2017-12-28T07:32:11Z
dc.date.issued2016-11-25
dc.identifier.urihttp://hdl.handle.net/10754/626468
dc.description.abstractThe inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updating of parameters in a computational model. This is a filter acting on random variables, and while its Monte Carlo variant --- the Ensemble Kalman Filter (EnKF) --- is fairly straightforward, we subsequently only sketch its implementation with the help of functional representations.
dc.publisherarXiv
dc.relation.urlhttp://arxiv.org/abs/1611.09293v1
dc.relation.urlhttp://arxiv.org/pdf/1611.09293v1
dc.rightsArchived with thanks to arXiv
dc.titleBayesian Parameter Estimation via Filtering and Functional Approximations
dc.typePreprint
dc.contributor.departmentExtreme Computing Research Center
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.eprint.versionPre-print
dc.contributor.institutionInstitute of Scientific Computing Technische Universität Braunschweig, Germany
dc.identifier.arxivid1611.09293
kaust.personLitvinenko, Alexander
dc.versionv1
refterms.dateFOA2018-06-14T03:36:33Z


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