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Fusion of Probabilistic Unreliable Indirect Information into Estimation Serving to Decision Making
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SYSNO ASEP 0543464 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Fusion of Probabilistic Unreliable Indirect Information into Estimation Serving to Decision Making Author(s) Kárný, Miroslav (UTIA-B) RID, ORCID
Hůla, František (UTIA-B)Number of authors 2 Source Title International Journal of Machine Learning and Cybernetics. - : Springer - ISSN 1868-8071
Roč. 12, č. 12 (2021), s. 3367-3378Number of pages 19 s. Publication form Print - P Language eng - English Country DE - Germany Keywords distributed data fusion ; information fusion ; Bayesian paradigm ; decision making ; parameter estimation ; multi-agent Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects LTC18075 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Method of publishing Limited access Institutional support UTIA-B - RVO:67985556 UT WOS 000665682400001 EID SCOPUS 85117794760 DOI 10.1007/s13042-021-01359-9 Annotation Bayesian decision making (DM) quantifies information by the probability density (pd) of treated variables. Gradual accumulation of information during acting increases the DM quality reachable by an agent exploiting it. The inspected accumulation way uses a parametric model forecasting observable DM outcomes and updates the posterior pd of its unknown parameter. In the thought multi-agent case, a neighbouring agent, moreover, provides a privately-designed pd forecasting the same observation. This pd may notably enrich the information of the focal agent. Bayes' rule is a unique deductive tool for a lossless compression of the information brought by the observations. It does not suit to processing of the forecasting pd. The paper extends solutions of this case. It: a) refines the Bayes'-rule-like use of the neighbour's forecasting pd. b) deductively complements former solutions so that the learnable neighbour's reliability can be taken into account. c) specialises the result to the exponential family, which shows the high potential of this information processing. d) cares about exploiting population statistics. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2022 Electronic address https://link.springer.com/article/10.1007/s13042-021-01359-9
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