Parameter identification in a probabilistic setting

Bojana V. Rosić, Anna Kučerová, Jan Sýkora, Oliver Pajonk, Alexander Litvinenko, Hermann G. Matthies*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

34 Citations (Scopus)
46 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)179-196
Number of pages18
JournalEngineering Structures
Volume50
DOIs
Publication statusPublished - 1 May 2013
Externally publishedYes

Keywords

  • Kalman filter
  • Linear bayes
  • Non-Gaussian Bayesian update
  • Parameter identification
  • Polynomial chaos

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