Towards Distributed Bayesian Estimation A Short Note on Selected Aspects

0368319 - ÚTIA 2012 RIV CZ eng C - Konferenční příspěvek (zahraniční konf.)
Dedecius, Kamil - Sečkárová, Vladimíra
Towards Distributed Bayesian Estimation A Short Note on Selected Aspects.
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. 67-72. 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 ČR GA102/08/0567
Výzkumný záměr: CEZ:AV0Z10750506
Klíčová slova: efficient estimation * a linear or nonlinear model * distributed estimation * Bayesian decision making
Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
http://library.utia.cas.cz/separaty/2011/AS/dedecius-towards distributed bayesian estimation a short note on selected aspects.pdf

The rapid development of ad-hoc wireless networks, sensor networks and similar calls for efficient estimation of common parameters of a linear or nonlinear model used to describe the operating environment. Therefore, the theory of collaborative distributed estimation has attained a very considerable focus in the past decade, however, mostly in the classical deterministic realm. We conjecture, that the consistent and versatile Bayesian decision making framework, whose applications range from the basic probability counting up to the nonlinear estimation theory, can significantly contribute to the distributed estimation theory. The limited extent of the paper allows to address the considered problem only very superficially and shortly. Therefore, we are forced to leave the rigorous approach in favor of a short survey indicating the arising possibilities appealing to the non- Bayesian literature.
Trvalý link: http://hdl.handle.net/11104/0202699