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Employing Bayesian Networks for Subjective Well-being Prediction
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SYSNO ASEP 0490308 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Employing Bayesian Networks for Subjective Well-being Prediction Author(s) Švorc, Jan (UTIA-B)
Vomlel, Jiří (UTIA-B) RID, ORCIDNumber of authors 2 Source Title 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 Pages s. 189-204 Number of pages 16 s. Publication form Print - P Action Workshop on Uncertainty Processing (WUPES’18) Event date 06.06.2018 - 09.06.2018 VEvent location Třeboň Country CZ - Czech Republic Event type WRD Language eng - English Country CZ - Czech Republic Keywords Subjective well-being ; Bayesian networks Subject RIV AO - Sociology, Demography OECD category Cultural and economic geography R&D Projects GA17-08182S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2019
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