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Model Selection Using Response Measurements: Bayesian Probabilistic Approach

Beck, James L. and Yuen, Ka-Veng (2004) Model Selection Using Response Measurements: Bayesian Probabilistic Approach. Journal of Engineering Mechanics, 130 (2). pp. 192-203. ISSN 0733-9399. http://resolver.caltech.edu/CaltechAUTHORS:20120810-112314239

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://ascelibrary.org/doi/abs/10.1061/%28ASCE%290733-9399%282004%29130%3A2%28192%29PublisherArticle
http://dx.doi.org/10.1061/(ASCE)0733-9399(2004)130:2(192) DOIArticle
Additional Information:©ASCE. The manuscript for this paper was submitted for review and possible publication on April 8, 2002; approved on July 8, 2003.
Subject Keywords:Bayesian analysis; Model studies; Time series analysis; Probabilistic methods; Mechanical systems, structural; Measurement; Excitation
Record Number:CaltechAUTHORS:20120810-112314239
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20120810-112314239
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:33087
Collection:CaltechAUTHORS
Deposited By: Sydney Garstang
Deposited On:13 Aug 2012 17:52
Last Modified:24 Feb 2014 19:07

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