Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/9490
Full metadata record
DC FieldValueLanguage
dc.rights.licenseopenAccess-
dc.contributor.authorRosić, Bojana-
dc.contributor.authorKucerova, Anna-
dc.contributor.authorSýkora J.-
dc.contributor.authorPajonk O.-
dc.contributor.authorLitvinenko, Alexander-
dc.contributor.authorMatthies H.-
dc.date.accessioned2020-09-19T18:25:06Z-
dc.date.available2020-09-19T18:25:06Z-
dc.date.issued2013-
dc.identifier.issn0141-0296-
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/9490-
dc.description.abstractThe parameters to be identified are described as random variables, the randomness reflecting the uncertainty about the true values, allowing the incorporation of new information through Bayes's theorem. Such a description has two constituents, the measurable function or random variable, and the probability measure. One group of methods updates the measure, the other group changes the function. We connect both with methods of spectral representation of stochastic problems, and introduce a computational procedure without any sampling which works completely deterministically, and is fast and reliable. Some examples we show have highly nonlinear and non-smooth behaviour and use non-Gaussian measures. © 2013 Elsevier Ltd.-
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.sourceEngineering Structures-
dc.titleParameter identification in a probabilistic setting-
dc.typearticle-
dc.identifier.doi10.1016/j.engstruct.2012.12.029-
dc.identifier.scopus2-s2.0-84875090700-
Appears in Collections:Faculty of Medical Sciences, Kragujevac

Page views(s)

515

Downloads(s)

56

Files in This Item:
File Description SizeFormat 
10.1016-j.engstruct.2012.12.029.pdf1.78 MBAdobe PDFThumbnail
View/Open


This item is licensed under a Creative Commons License Creative Commons