Fusion of Probabilistic Unreliable Indirect Information into Estimation Serving to Decision Making

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Fusion of Probabilistic Unreliable Indirect Information into Estimation Serving to Decision Making

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Fusion of Probabilistic Unreliable Indirect Information into Estimation Serving to Decision Making

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LTC18075" target="_blank" >LTC18075: Distribuované racionální rozhodování: kooperační aspekty</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Machine Learning and Cybernetics

ISSN

1868-8071

e-ISSN

1868-808X

Svazek periodika

12

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

19

Strana od-do

3367-3378

Kód UT WoS článku

000665682400001

EID výsledku v databázi Scopus

2-s2.0-85117794760