Počet záznamů: 1
Employing Bayesian Networks for Subjective Well-being Prediction
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SYSNO ASEP 0490308 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Employing Bayesian Networks for Subjective Well-being Prediction Tvůrce(i) Švorc, Jan (UTIA-B)
Vomlel, Jiří (UTIA-B) RID, ORCIDCelkový počet autorů 2 Zdroj.dok. Proceedings of the 11th Workshop on Uncertainty Processing (WUPES’18). - Praha : MatfyzPress, Publishing House of the Faculty of Mathematics and Physics Charles University, 2018 / Kratochvíl Václav ; Vejnarová Jiřina - ISBN 978-80-7378-361-7 Rozsah stran s. 189-204 Poč.str. 16 s. Forma vydání Tištěná - P Akce Workshop on Uncertainty Processing (WUPES’18) Datum konání 06.06.2018 - 09.06.2018 Místo konání Třeboň Země CZ - Česká republika Typ akce WRD Jazyk dok. eng - angličtina Země vyd. CZ - Česká republika Klíč. slova Subjective well-being ; Bayesian networks Vědní obor RIV AO - Sociologie, demografie Obor OECD Cultural and economic geography CEP GA17-08182S GA ČR - Grantová agentura ČR Institucionální podpora UTIA-B - RVO:67985556 Anotace This contribution aims at using Bayesian networks for modelling the relations between the individual subjective well-being (SWB) and the individual material situation. The material situation is approximated by subjective measures (perceived economic strain, subjective evaluation of the income relative to most people in the country and to own past) and objective measures (household’s income, material deprivation, financial problems and housing defects). The suggested Bayesian network represents the relations among SWB and the variables approximating the material situation. The structure is established based on the expertise gained from literature, whereas the parameters are learnt based on empirical data from 3rd edition of European Quality of Life Study for the Czech Republic, Hungary, Poland and Slovakia conducted in 2011. Prediction accuracy of SWB is tested and compared with two benchmark models whose structures are learnt using Gobnilp software and a greedy algorithm built in Hugin software. SWB prediction accuracy of the expert model is 66,83%, which is significantly different from no information rate of 55,16%. It is slightly lower than the two machine learnt benchmark models. Pracoviště Ústav teorie informace a automatizace Kontakt Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Rok sběru 2019
Počet záznamů: 1