0543464 - ÚTIA 2022 RIV DE eng J - Journal Article
Kárný, Miroslav - Hůla, František
Fusion of Probabilistic Unreliable Indirect Information into Estimation Serving to Decision Making.
International Journal of Machine Learning and Cybernetics. Roč. 12, č. 12 (2021), s. 3367-3378. ISSN 1868-8071. E-ISSN 1868-808X
R&D Projects: GA MŠMT(CZ) LTC18075
Grant - others:The European Cooperation in Science and Technology (COST)(XE) CA16228
Institutional support: RVO:67985556
Keywords : distributed data fusion * information fusion * Bayesian paradigm * decision making * parameter estimation * multi-agent
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 4.377, year: 2021 ; AIS: 0.597, rok: 2021
Method of publishing: Limited access
Result website:
http://library.utia.cas.cz/separaty/2021/AS/karny-0543464.pdf https://link.springer.com/article/10.1007/s13042-021-01359-9

DOI: https://doi.org/10.1007/s13042-021-01359-9

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.
Permanent Link: http://hdl.handle.net/11104/0320767