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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F14%3A00438275%21RIV15-GA0-67985556
rdf:type
skos:Concept n16:Vysledek
dcterms:description
An exploitation of prior knowledge in parameter estimation becomes vital whenever measured data is not informative enough. Elicitation of quantified prior knowledge is a well-elaborated art in societal and medical applications but not in the engineering ones. Frequently required involvement of a facilitator is mostly unrealistic due to either facilitator’s high costs or complexity of modelled relationships that cannot be grasped by humans. This paper provides a facilitator-free approach based on an advanced knowledgesharing methodology. It presents the approach on commonly available types of knowledge and applies the methodology to a normal controlled autoregressive model. An exploitation of prior knowledge in parameter estimation becomes vital whenever measured data is not informative enough. Elicitation of quantified prior knowledge is a well-elaborated art in societal and medical applications but not in the engineering ones. Frequently required involvement of a facilitator is mostly unrealistic due to either facilitator’s high costs or complexity of modelled relationships that cannot be grasped by humans. This paper provides a facilitator-free approach based on an advanced knowledgesharing methodology. It presents the approach on commonly available types of knowledge and applies the methodology to a normal controlled autoregressive model.
dcterms:title
Fully probabilistic knowledge expression and incorporation Fully probabilistic knowledge expression and incorporation
skos:prefLabel
Fully probabilistic knowledge expression and incorporation Fully probabilistic knowledge expression and incorporation
skos:notation
RIV/67985556:_____/14:00438275!RIV15-GA0-67985556
n3:aktivita
n6:I n6:S n6:P
n3:aktivity
I, P(GA13-13502S), S
n3:cisloPeriodika
4
n3:dodaniDat
n13:2015
n3:domaciTvurceVysledku
n11:4780280 n11:4029798 n11:6585256
n3:druhVysledku
n5:J
n3:duvernostUdaju
n14:S
n3:entitaPredkladatele
n10:predkladatel
n3:idSjednocenehoVysledku
17641
n3:idVysledku
RIV/67985556:_____/14:00438275
n3:jazykVysledku
n18:eng
n3:klicovaSlova
Bayesian estimation; knowledge elicitation; just-in-time modelling; controlled autoregressive model
n3:klicoveSlovo
n7:just-in-time%20modelling n7:controlled%20autoregressive%20model n7:knowledge%20elicitation n7:Bayesian%20estimation
n3:kodStatuVydavatele
US - Spojené státy americké
n3:kontrolniKodProRIV
[77F0914E2862]
n3:nazevZdroje
Statistics and its Interface
n3:obor
n17:BB
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
6
n3:projekt
n15:GA13-13502S
n3:rokUplatneniVysledku
n13:2014
n3:svazekPeriodika
7
n3:tvurceVysledku
Kárný, Miroslav Ruggeri, F. Guy, Tatiana Valentine Bodini, A. Kracík, J. Nedoma, Petr
n3:wos
000348624200008
s:issn
1938-7989
s:numberOfPages
13
n9:doi
10.4310/SII.2014.v7.n4.a7