"Parameter identification in a probabilistic setting"@en . "Rosi\u0107, B." . "0141-0296" . "GB - Spojen\u00E9 kr\u00E1lovstv\u00ED Velk\u00E9 Brit\u00E1nie a Severn\u00EDho Irska" . "21110" . "May" . "10.1016/j.engstruct.2012.12.029" . . . "50" . . . "18"^^ . "Matthies, H. G." . . . . "Parameter identification; Non-Gaussian Bayesian update; Linear Bayes; Kalman filter; Polynomial chaos"@en . . . . "95313" . "Engineering Structures" . "P(GAP105/11/0411), P(GAP105/12/1146), P(MEB101105)" . "Ku\u010Derov\u00E1, Anna" . "6"^^ . "Pajonk, O." . . . "Litvinenko, A." . "Parameter identification in a probabilistic setting"@en . . . "Parameter identification in a probabilistic setting" . "2"^^ . . . "S\u00FDkora, Jan" . . "Parameter identification in a probabilistic setting" . . "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\u2019s 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."@en . "http://dx.doi.org/10.1016/j.engstruct.2012.12.029" . "000317457000018" . . . . "RIV/68407700:21110/13:00202999!RIV14-MSM-21110___" . "[E051B1774C30]" . "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\u2019s 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." . "RIV/68407700:21110/13:00202999" .