Bayesian Parameter Estimation via Filtering and Functional Approximations

Handle URI:
http://hdl.handle.net/10754/626468
Title:
Bayesian Parameter Estimation via Filtering and Functional Approximations
Authors:
Matthies, Hermann G.; Litvinenko, Alexander ( 0000-0001-5427-3598 ) ; Rosic, Bojana V.; Zander, Elmar
Abstract:
The 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.
KAUST Department:
Extreme Computing Research Center; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
arXiv
Issue Date:
25-Nov-2016
ARXIV:
arXiv:1611.09293
Type:
Preprint
Additional Links:
http://arxiv.org/abs/1611.09293v1; http://arxiv.org/pdf/1611.09293v1
Appears in Collections:
Other/General Submission; Other/General Submission; Extreme Computing Research Center; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorMatthies, Hermann G.en
dc.contributor.authorLitvinenko, Alexanderen
dc.contributor.authorRosic, Bojana V.en
dc.contributor.authorZander, Elmaren
dc.date.accessioned2017-12-28T07:32:11Z-
dc.date.available2017-12-28T07:32:11Z-
dc.date.issued2016-11-25en
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.en
dc.publisherarXiven
dc.relation.urlhttp://arxiv.org/abs/1611.09293v1en
dc.relation.urlhttp://arxiv.org/pdf/1611.09293v1en
dc.rightsArchived with thanks to arXiven
dc.titleBayesian Parameter Estimation via Filtering and Functional Approximationsen
dc.typePreprinten
dc.contributor.departmentExtreme Computing Research Centeren
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.eprint.versionPre-printen
dc.contributor.institutionInstitute of Scientific Computing Technische Universit├Ąt Braunschweig, Germanyen
dc.identifier.arxividarXiv:1611.09293en
kaust.authorLitvinenko, Alexanderen
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