0598454 20250127074334.720240923235959.9 001347210900028 16 s. E cav_un_epca*0599192Proceedings of Machine Learning Research (PMLR), Volume 246 : International Conference on Probabilistic Graphical Models470-485San DiegoJMLR-JOURNAL MACHINE LEARNING RESEARCH2024 Bayesian Networks Data Analysis Structural Learning of Bayesian Networks Actively Open-minded Thinking Conspiracy Theories cav_un_auth*0101228 Vomlel Jiří UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory 55 K Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0016442 Kuběna A. CZ 15 cav_un_auth*0101206 Šmíd Martin UTIA-B Ekonometrie Department of Econometrics E E Department of Econometrics 15 Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0471876 Weinerová J. GB 15 https://library.utia.cas.cz/separaty/2024/MTR/vomlel-0598454.pdf https://proceedings.mlr.press/v246/vomlel24a.html CZ.02.01.01/00/22 008/0004595 Ministerstvo školství, mládeže a tělovýchovy - GA MŠk CZ cav_un_auth*0473001 Bayesian networks (BNs) represent a probabilistic model that can visualize relationships between variables. We apply various BN structure learning algorithms to a large dataset from a Czech university entrance exam. This dataset includes a test of active, open-minded thinking designed by Jonathan Baron, as well as a test of students’ attitudes toward various conspiracies. Using BNs, we were able to identify the structure of the conspiracies and their relationships with active open-minded thinking. We also compared results of different BN structure learning algorithms with results of selected standard data analysis methods. cav_un_auth*0473000 International Conference on Probabilistic Graphical Models 2024 /12./ 20240911 20240913 Nijmegen NL BB 50000 50400 50401 2025 4 PR RVO:67985556 https://hdl.handle.net/11104/0356715 cav_un_auth*0295413 Univerzita Karlova v Praze, Filozofická fakulta (CZ) FF UK CZ S 2024 C SCOPUS PUBMED 001347210900028 WOS online 666 kB cav_un_epca*0599192 Proceedings of Machine Learning Research (PMLR), Volume 246 : International Conference on Probabilistic Graphical Models 2640-3498 470 485 San Diego JMLR-JOURNAL MACHINE LEARNING RESEARCH 2024