Original title: Employing Bayesian Networks for Subjective Well-being Prediction
Authors: Švorc, Jan ; Vomlel, Jiří
Document type: Papers
Conference/Event: Workshop on Uncertainty Processing (WUPES’18), Třeboň (CZ), 20180606
Year: 2018
Language: eng
Abstract: 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.
Keywords: Bayesian networks; Subjective well-being
Project no.: GA17-08182S (CEP)
Funding provider: GA ČR
Host item entry: Proceedings of the 11th Workshop on Uncertainty Processing (WUPES’18), ISBN 978-80-7378-361-7

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/2018/MTR/svorc-0490308.pdf
Original record: http://hdl.handle.net/11104/0284593

Permalink: http://www.nusl.cz/ntk/nusl-375671


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Research > Institutes ASCR > Institute of Information Theory and Automation
Conference materials > Papers
 Record created 2018-06-19, last modified 2019-03-28


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