Model selection using response measurements: Bayesian probabilistic approach
Beck J.L.; Yuen K.-V.
Source PublicationJournal of Engineering Mechanics
AbstractA Bayesian probabilistic approach is presented for selecting the most plausible class of models for a structural or mechanical system within some specified set of model classes, based on system response data. The crux of the approach is to rank the classes of models based on their probabilities conditional on the response data which can be calculated based on Bayes' theorem and an asymptotic expansion for the evidence for each model class. The approach provides a quantitative expression of a principle of model parsimony or of Ockham's razor which in this context can be stated as "simpler models are to be preferred over unnecessarily complicated ones." Examples are presented to illustrate the method using a single-degree-of-freedom bilinear hysteretic system, a linear two-story frame, and a ten-story shear building, all of which are subjected to seismic excitation.
KeywordBayesian analysis Excitation Measurement Mechanical systems Model studies Probabilistic methods Structural Time series analysis
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Cited Times [WOS]:288   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
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GB/T 7714
Beck J.L.,Yuen K.-V.. Model selection using response measurements: Bayesian probabilistic approach[J]. Journal of Engineering Mechanics,2004,130(2):192.
APA Beck J.L.,&Yuen K.-V..(2004).Model selection using response measurements: Bayesian probabilistic approach.Journal of Engineering Mechanics,130(2),192.
MLA Beck J.L.,et al."Model selection using response measurements: Bayesian probabilistic approach".Journal of Engineering Mechanics 130.2(2004):192.
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