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Towards Distributed Bayesian Estimation A Short Note on Selected Aspects
Dedecius, Kamil ; Sečkárová, Vladimíra
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.
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Automated Preferences Elicitation
Kárný, Miroslav ; Guy, Tatiana Valentine
Systems supporting decision making became almost inevitable in the modern complex world. Their efficiency depends on the sophisticated interfaces enabling a user take advantage of the support while respecting the increasing on-line information and incomplete, dynamically changing user’s preferences. The best decision making support is useless without the proper preference elicitation. The paper proposes a methodology supporting automatic learning of quantitative description of preferences. The proposed elicitation serves to fully probabilistic design, which is an extension of Bayesian decision making.
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Supra-Bayesian Approach to Merging of Incomplete and Incompatible Data
Sečkárová, Vladimíra
In practice we often need to take every available information into account. Unfortunately the pieces of information given by different sources are often incomplete (with respect to what we are interested in) and have different forms. In this work we try to solve the problem of treating such data in order to get an optimal merger of them. We present a systematic and unified way how to combine the pieces of information by using a Supra-Bayesian approach and other mathematical tools, e.g. Kerridge inaccuracy, maximum entropy principle. To show how the proposed method works a simple example is given at the end of the work.
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Metody spolupráce v bayesovském rozhodování s víceúčastníky - rukopis PhD. práce
Kracík, Jan
This work has its origin in the GAAV project 1ET100750401 BADDYR – Bayesian Adaptive Distributed Decision Making – solved in the Department of Adaptive Systems in the Institute of Information Theory and Automation in years 2004-2007. The objective of the BADDYR project was to develop a theoretical and algorithmic background for a distributed decision making with multiple Bayesian decision makers. The results of the BADDYR project are further developed in the GAˇCR project 102/08/0567 – Fully probabilistic design of dynamic decision strategies. This work contributes to these projects by a design of methods for communication of the decision makers.
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O bayesovském rozhodování s více účastníky
Kracík, Jan
This report briefly summarizes the perspective on decision making with multiple Bayesian decision makers which has been reached during the work on the GAAV project 1ET100750401 BADDYR. It gradually turned out that the problem is much deeper than it may appear. At the same time we foud out that in some special, yet practically important, cases a suitable solution could be reached withou any novel methods just via detailed modelling of knowledge and preferences of individual particiapns. This special cases also serve us as a motivation for solution of more general problems.
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Spojování znalostí jako problém odhadování
Kárný, Miroslav ; Bodini, A. ; Ruggeri, F.
Bayesian cooperative decision making by multiple participants requires a combination of environment models of cooperating neighbors. In a sense, a speci¯c participant tries to exploit imprecise probabilistic knowledge con- cerning of low-dimensional marginal and conditional distributions o®ered by his neighbors. In spite of the substantial overlap with domains like knowl- edge elicitation, estimation of graphical models or building of probabilistic expert systems, the merging problem was not addressed yet in its entirety.
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