0543464 20240103225940.820210625235959.9 000665682400001 85117794760 10.1007/s13042-021-01359-9 19 s. P cav_un_epca*04610481868-80711212 (2021)3367-3378Springer distributed data fusion information fusion Bayesian paradigm decision making parameter estimation multi-agent cav_un_auth*0101124 Kárný Miroslav UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS 50 A Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0333671 Hůla František UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS CZ 50 Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2021/AS/karny-0543464.pdf https://link.springer.com/article/10.1007/s13042-021-01359-9 LTC18075 GA MŠk CZ cav_un_auth*0372050 CA16228 The European Cooperation in Science and Technology (COST) XE cav_un_auth*0372051 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. WOS IN 10000 10200 10201 2022 2 4 A sml 4as 20231122145809.0 RVO:67985556 http://hdl.handle.net/11104/0320767 S Licence to Publish 20210528 3+4 Article Computer Science Artificial Intelligence C COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE 3.764 0.637 2.7 0.0051 0.597 4115 73 1.003 46.47 4.59 2590 Q2 3.862 234 Q2 61 Q3 Q2 0.85 Q2 61.034 2021 Soubory v repozitáři: karny-0543464-PublicationAgreement.pdf 85117794760 SCOPUS PUBMED 000665682400001 WOS cav_un_epca*0461048 International Journal of Machine Learning and Cybernetics 1868-8071 1868-808X Roč. 12 č. 12 2021 3367 3378 Springer