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Variational Bayes in Distributed Fully Probabilistic Decision Making

SYSNO ASEP	0368318
Document Type	C - Proceedings Paper (int. conf.)
R&D Document Type	Conference Paper
Title	Variational Bayes in Distributed Fully Probabilistic Decision Making
Author(s)	Šmídl, Václav (UTIA-B)_{RID, ORCID} Tichý, Ondřej (UTIA-B)_{RID, ORCID}
Number of authors	2
Source Title	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 - ISBN 978-80-903834-6-3
Pages	s. 73-80
Number of pages	8 s.
Action	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)
Event date	16.12.2011-16.12.2011
VEvent location	Sierra Nevada
Country	ES - Spain
Event type	WRD
Language	eng - English
Country	CZ - Czech Republic
Keywords	Fully Probabilistic Design ; Variational Bayes method ; distributed control
Subject RIV	BB - Applied Statistics, Operational Research
R&D Projects	1M0572 GA MŠMT - Ministry of Education, Youth and Sports (MEYS)
	TA01030603 GA TA ČR - Technology Agency of the Czech Republic (TA ČR)
CEZ	AV0Z10750506 - UTIA-B (2005-2011)
Annotation	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.
Workplace	Institute of Information Theory and Automation
Contact	Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.
Year of Publishing	2012

Number of the records: 1