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Bayesian Updating of Nonlinear Model Predictions using Markov Chain Monte Carlo Simulation

Beck, James L. and Au, S. K. and Yuen, K.-V. (2001) Bayesian Updating of Nonlinear Model Predictions using Markov Chain Monte Carlo Simulation. In: 18th Biennial Conference on Mechanical Vibration and Noise. Vol.6 Part A. American Society of Mechanical Engineers , New York, NY, pp. 821-828. ISBN 0791835464.

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The usual practice in system identification is to use system data to identify one model from a set of possible models and then to use this model for predicting system behavior. In contrast, the present robust predictive approach rigorously combines the predictions of all the possible models, appropriately weighted by their updated probabilities based on the data. This Bayesian system identification approach is applied to update the robust reliability of a dynamical system based on its measured response time histories. A Markov chain simulation method based on the Metropolis-Hastings algorithm and an adaptive scheme is proposed to evaluate the robust reliability integrals. An example for updating the reliability of a Duffing oscillator is given to illustrate the proposed method.

Item Type:Book Section
Yuen, K.-V.0000-0002-1755-6668
Additional Information:This paper is based upon work partly supported by the Pacific Earthquake Engineering Research Center under National Science Foundation Cooperative Agreement No. CMS-9701568. This support is gratefully acknowledged.
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Record Number:CaltechAUTHORS:20120924-143920520
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:34324
Deposited By: Carmen Nemer-Sirois
Deposited On:16 Nov 2012 23:00
Last Modified:12 Aug 2021 22:45

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