Fusion of Probabilistic Unreliable Indirect Information into Estimation Serving to Decision Making
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00543464" target="_blank" >RIV/67985556:_____/21:00543464 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s13042-021-01359-9" target="_blank" >https://link.springer.com/article/10.1007/s13042-021-01359-9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s13042-021-01359-9" target="_blank" >10.1007/s13042-021-01359-9</a>
Alternative languages
Result language
angličtina
Original language name
Fusion of Probabilistic Unreliable Indirect Information into Estimation Serving to Decision Making
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LTC18075" target="_blank" >LTC18075: Distributed rational decision making: cooperation aspects</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Machine Learning and Cybernetics
ISSN
1868-8071
e-ISSN
1868-808X
Volume of the periodical
12
Issue of the periodical within the volume
12
Country of publishing house
DE - GERMANY
Number of pages
19
Pages from-to
3367-3378
UT code for WoS article
000665682400001
EID of the result in the Scopus database
2-s2.0-85117794760