bibtype C - Conference Paper (international conference)
ARLID 0639049
utime 20251006140333.3
mtime 20250918235959.9
title (primary) (eng) Fuzzy Bayesian Networks with Likert Scales
specification
page_count 12 s.
media_type P
serial
ARLID cav_un_epca*0636591
ISBN 978-80-7378-525-3
title Proceedings of the 13th Workshop on Uncertainty Processing (WUPES’25)
page_num 164-175
publisher
place Prague
name MatfyzPress
year 2025
editor
name1 Studený
name2 Milan
editor
name1 Ay
name2 Nihat
editor
name1 Capotorti
name2 Andrea
editor
name1 Csirmaz
name2 László
editor
name1 Jiroušek
name2 Radim
editor
name1 Kleiter
name2 Gernot D.
editor
name1 Shenoy
name2 Prakash P.
keyword Bayesian networks
keyword Fuzzy theory
keyword uncertainty in artificicial intelligence
author (primary)
ARLID cav_un_auth*0493360
name1 Mrógala
name2 J.
country CZ
author
ARLID cav_un_auth*0212605
name1 Perfilieva
name2 I.
country CZ
author
ARLID cav_un_auth*0101228
name1 Vomlel
name2 Jiří
institution UTIA-B
full_dept (cz) Matematická teorie rozhodování
full_dept Department of Decision Making Theory
department (cz) MTR
department MTR
full_dept Department of Decision Making Theory
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url https://library.utia.cas.cz/separaty/2025/MTR/vomlel-0639049.pdf
cas_special
project
project_id CZ.02.01.01/00/23_021/000859
agency EC
country CZ
ARLID cav_un_auth*0493362
abstract (eng) Our work is motivated by the applications of probabilistic models in the social sciences, in which surveys and questionnaires are typically used to collect respondents' opinions via a Likert scale. The dividing lines between the states on the Likert scale are vague, so it is natural to interpret them using fuzzy numbers instead of integers. We treat the true model variables as hidden continuous variables, the values of which are observed only through their fuzzified counterparts. This approach seems more conceptually appropriate in the context of surveys and questionnaires, since the modeled variables are continuous by nature but are only observed on a fuzzy, discrete scale. Probabilistic inference with continuous variables is challenging when the assumption of normality of the variables' distribution is violated, which is particularly true for variables modeling polarizing issues. We approximate continuous, multidimensional probability distributions using an F-transform composed of basic functions with central points, called nodes, at a multidimensional grid. We illustrate the suggested approach using a small Bayesian network model of data from the survey ``Dividing Lines in Czech Society.''
action
ARLID cav_un_auth*0493361
name Workshop on Uncertainty Processing (WUPES’25)
dates 20250604
mrcbC20-s 20250607
place Třešť
country CZ
RIV JD
FORD0 10000
FORD1 10200
FORD2 10201
reportyear 2026
num_of_auth 3
presentation_type PR
inst_support RVO:67985556
permalink https://hdl.handle.net/11104/0370049
cooperation
ARLID cav_un_auth*0418144
name Ostravská univerzita
institution OSU
country CZ
confidential S
arlyear 2025
mrcbU14 SCOPUS
mrcbU24 PUBMED
mrcbU34 WOS
mrcbU63 cav_un_epca*0636591 Proceedings of the 13th Workshop on Uncertainty Processing (WUPES’25) 978-80-7378-525-3 164 175 Prague MatfyzPress 2025 719
mrcbU67 Studený Milan 340
mrcbU67 Ay Nihat 340
mrcbU67 Capotorti Andrea 340
mrcbU67 Csirmaz László 340
mrcbU67 Jiroušek Radim 340
mrcbU67 Kleiter Gernot D. 340
mrcbU67 Shenoy Prakash P. 340