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Towards a Supra-Bayesian Approach to Merging of Information
Sečkárová, Vladimíra
Merging of information given by different decision makers (DMs) has become a much discussed topic in recent years and many procedures were developed towards it. The main and the most discussed problem is the incompleteness of given information. Little attention is paid to the possible forms in which the DMs provide them; in most of cases arising procedures are working only for a particular type of information. Recently introduced Supra-Bayesian approach to merging of information brings a solution to two previously mentioned problems. All is based on a simple idea of unifying all given information into one form and treating the possible incompleteness. In this article, beside a brief repetition of the method, we show, that the constructed merger of information reduces to the Bayesian solution if information calls for this.
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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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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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