bibtype V - Research Report
ARLID 0315659
utime 20240111140710.9
mtime 20081202235959.9
title (primary) (eng) Fully probabilistic knowledge expression and incorporation
publisher
place Milano
name Istituto di Matematica Applicata e Tecnologie Informatiche
pub_time 2008
specification
page_count 28 s.
media_type www
edition
name Research Report-Istituto di Matematica Applicata e Tecnologie Informatiche
volume_id 8-10MI
keyword Bayesian estimation
keyword prior knowledge
keyword automatised knowledge elicitation
author (primary)
ARLID cav_un_auth*0101124
name1 Kárný
name2 Miroslav
institution UTIA-B
full_dept Department of Adaptive Systems
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101061
name1 Andrýsek
name2 Josef
institution UTIA-B
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0205835
name1 Bodini
name2 A.
country IT
author
ARLID cav_un_auth*0101092
name1 Guy
name2 Tatiana Valentine
institution UTIA-B
full_dept Department of Adaptive Systems
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101138
name1 Kracík
name2 Jan
institution UTIA-B
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101168
name1 Nedoma
name2 Petr
institution UTIA-B
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0205836
name1 Ruggeri
name2 F.
country IT
source
source_type textovy soubor
url http://library.utia.cas.cz/separaty/2008/AS/karny-fully probabilistic knowledge expression and incorporation.pdf
cas_special
project
project_id 1M0572
agency GA MŠk
ARLID cav_un_auth*0001814
project
project_id 2C06001
agency GA MŠk
ARLID cav_un_auth*0217685
project
project_id GA102/08/0567
agency GA ČR
ARLID cav_un_auth*0239566
research CEZ:AV0Z10750506
abstract (eng) Exploitation of prior knowledge in parameter estimation is vital whenever data is not informative enough. Elicitation and quantification of prior knowledge is a well-elaborated art in social and medical appliations but not in engineering ones. Frequently required involvment of a facilitator is mostly unrealistic due to either facilitators' high costs or the high complexitu of modelled relationships that cannot be grasped by the human. This paper provides a facilitator-free approach exploiting a methodology of knowledge sharing. The considered task assumes prospective models be indexed by an unknown finite-dimensional parameter. The parameter is estimated using (i) observed data; (ii) a prior probability density function (pdf); and (iii) uncertain expert's information on the modelled data. The parametric model specifies pdf of the system's output conditioned on realised data and parameter.
reportyear 2009
RIV BB
mrcbC52 4 O 4o 20231122133745.9
permalink http://hdl.handle.net/11104/0165800
arlyear 2008
mrcbTft \nSoubory v repozitáři: 0315659.pdf
mrcbU10 2008
mrcbU10 Milano Istituto di Matematica Applicata e Tecnologie Informatiche
mrcbU56 textovy soubor