Employing Bayesian Networks for Subjective Well-being Prediction

0490308 - ÚTIA 2019 RIV CZ eng C - Konferenční příspěvek (zahraniční konf.)
Švorc, Jan - Vomlel, Jiří
Employing Bayesian Networks for Subjective Well-being Prediction.
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, V.; Vejnarová, J.), s. 189-204. ISBN 978-80-7378-361-7.
[Workshop on Uncertainty Processing (WUPES’18). Třeboň (CZ), 06.06.2018-09.06.2018]
Grant CEP: GA ČR GA17-08182S
Institucionální podpora: RVO:67985556
Klíčová slova: Subjective well-being * Bayesian networks
Obor OECD: Cultural and economic geography
http://library.utia.cas.cz/2018/MTR/svorc-0490308.pdf

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.
Trvalý link: http://hdl.handle.net/11104/0284593