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A Bayesian-Decision Theoretic Approach to Model Error Modeling

McVinish, Ross S., Braslavsky, Julio H., & Mengersen, Kerrie L. (2007) A Bayesian-Decision Theoretic Approach to Model Error Modeling. In Preprints of the 14th IFAC Symposium on System Identification - SYSID-2006, 29-31 March 2006, Newcastle, Australia.

Abstract

This paper takes a Bayesian-decision theoretic approach to transfer function estimation, nominal model estimation, and quantification of the resulting model error. Consistency of the nonparametric estimate of the transfer function is proved together with a rate of convergence. The required quantities can be computed routinely using reversible jump Markov chain Monte Carlo methods. The proposed methodology has connections with set membership identification which has been extensively studied for this problem.

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220 since deposited on 07 Nov 2008
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ID Code: 15526
Item Type: Conference Paper
Additional URLs:
Keywords: Transfer functions, Decision theory, Non, parametric identification, Monte Carlo calculation, loss minimization
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Statistical Theory (010405)
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2007 IFAC, the International Federation of Automatic Control
Copyright Statement: Reproduced in accordance with the copyright policy of the publisher.
Deposited On: 07 Nov 2008
Last Modified: 29 Feb 2012 23:40

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