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Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill
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SYSNO ASEP 0342595 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill Author(s) Raftery, A. E. (US)
Kárný, Miroslav (UTIA-B) RID, ORCID
Ettler, P. (CZ)Source Title Technometrics - ISSN 0040-1706
Volume 52, Number 1 (2010), s. 52-66Number of pages 15 s. Language eng - English Country US - United States Keywords prediction ; rolling mills ; Bayesian Dynamic Averaging Subject RIV BC - Control Systems Theory R&D Projects 1M0572 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) 7D09008 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) CEZ AV0Z10750506 - UTIA-B (2005-2011) UT WOS 000275920200006 EID SCOPUS 77949408057 DOI 10.1198/TECH.2009.08104 Annotation We consider the problem of online prediction when it is uncertain what the best prediction model to use is. We develop a method called Dynamic Model Averaging (DMA) in which a state space model for the parameters of each model is combined with a Markov chain model for the correct model. This allows the "correct" model to vary over time. The state space and Markov chain models are both specied in terms of forgetting, leading to a highly parsimonious representation. As a special case, when the model and parameters do not change, DMA is a recursive implementation of standard Bayesian model averaging, which we call recursive model averaging (RMA). The method is applied to the problem of predicting the output strip thickness for a cold rolling mill, where the output is measured with a time delay. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2011
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