Počet záznamů: 1

Variational Bayes in Distributed Fully Probabilistic Decision Making

  1. 1.
    0368318 - UTIA-B 2012 RIV CZ eng C - Konferenční příspěvek (zahraniční konf.)
    Šmídl, Václav - Tichý, Ondřej
    Variational Bayes in Distributed Fully Probabilistic Decision Making.
    The 2nd International Workshop od Decision Making with Multiple Imperfect Decision Makers. Held in Conjunction with the 25th Annual Conference on Neural Information Processing Systems (NIPS 2011). Prague: Institute of Information Theory and Automation, 2011, s. 73-80. ISBN 978-80-903834-6-3.
    [The 2nd International Workshop od Decision Making with Multiple Imperfect Decision Makers. Held in Conjunction with the 25th Annual Conference on Neural Information Processing Systems (NIPS 2011). Sierra Nevada (ES), 16.12.2011-16.12.2011]
    Grant CEP: GA MŠk 1M0572; GA TA ČR TA01030603
    Výzkumný záměr: CEZ:AV0Z10750506
    Klíčová slova: Fully Probabilistic Design * Variational Bayes method * distributed control
    Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
    http://library.utia.cas.cz/separaty/2011/AS/smidl-variational bayes in distributed fully probabilistic decision making.pdf http://library.utia.cas.cz/separaty/2011/AS/smidl-variational bayes in distributed fully probabilistic decision making.pdf

    We are concerned with design of decentralized control strategy for stochastic systems with global performance measure. It is possible to design optimal centralized control strategy, which often cannot be used in distributed way. The distributed strategy then has to be suboptimal (imperfect) in some sense. In this paper, we propose to optimize the centralized control strategy under the restriction of conditional independence of control inputs of distinct decision makers. Under this optimization, the main theorem for the Fully Probabilistic Design is closely related to that of the well known Variational Bayes estimation method. The resulting algorithm then requires communication between individual decision makers in the form of functions expressing moments of conditional probability densities. This contrasts to the classical Variational Bayes method where the moments are typically numerical.
    Trvalý link: http://hdl.handle.net/11104/0202698