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Bayesian averaging of regressive models

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    0346948 - ÚTIA 2011 RIV HU eng C - Conference Paper (international conference)
    Dedecius, Kamil - Jirsa, Ladislav - Pištěk, Miroslav
    Bayesian averaging of regressive models.
    Proceedings of the 11th International PhD Workshop on Systems and Control. Veszprém, Maďarsko: Faculty of Information Technology, University of Pannonia, 2010, s. 1-6. ISBN 978-615-5044-00-7.
    [11th International PhD Workshop on Systems and Control a Young Generation Viewpoint. Veszprém (HU), 01.09.2010-03.09.2010]
    R&D Projects: GA MŠMT(CZ) 7D09008
    Institutional research plan: CEZ:AV0Z10750506
    Keywords : bayesian modelling * model averaging * estimation
    Subject RIV: JD - Computer Applications, Robotics
    http://library.utia.cas.cz/separaty/2010/AS/dedecius-bayesian averaging of regressive models.pdf

    In the real world, it is often possible to model certain variables using several different regressive models. However, as the theoretical (mathematical, physical...) description of the real world is almost never perfect, there exists uncertainty about the true or best-fitting model. This paper deals with an issue of `mixing' information from multiple potentially true models, which run in parallel, to obtain a single outcome, taking the model uncertainty into account. While this issue has been addressed by many research papers in the past, most of them were developed for the static cases or for the state-space models. Here, we discuss an enhancement for a class of input-output regressive models. The described method allows to switch among models to reflect their modelling performance. If there is a single best model, the method quickly converges to it.
    Permanent Link: http://hdl.handle.net/11104/0187843

     
     
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