Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/9490
Title: Parameter identification in a probabilistic setting
Authors: Rosić, Bojana
Kucerova, Anna
Sýkora J.
Pajonk O.
Litvinenko, Alexander
Matthies H.
Issue Date: 2013
Abstract: The 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.
URI: https://scidar.kg.ac.rs/handle/123456789/9490
Type: article
DOI: 10.1016/j.engstruct.2012.12.029
ISSN: 0141-0296
SCOPUS: 2-s2.0-84875090700
Appears in Collections:Faculty of Medical Sciences, Kragujevac

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