Model Selection Using Response Measurements: Bayesian Probabilistic Approach
Publication: Journal of Engineering Mechanics
Volume 130, Issue 2
Abstract
A 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.
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Published In
Journal of Engineering Mechanics
Volume 130 • Issue 2 • February 2004
Pages: 192 - 203
Copyright
Copyright © 2004 American Society of Civil Engineers.
History
Received: Apr 8, 2002
Accepted: Jul 8, 2003
Published online: Jan 16, 2004
Published in print: Feb 2004
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