0598807 20250121145508.720241001235959.9 001323540900023 10.1007/978-3-031-65993-5_23 8 s. P cav_un_epca*0598452978-3-031-65992-82194-5357192-199ChamSpringer2024 belief functions entropy information content cav_un_auth*0101118 Jiroušek Radim UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0216188 Kratochvíl Václav UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory CZ Ústav teorie informace a automatizace AV ČR, v. v. i. https://library.utia.cas.cz/separaty/2024/MTR/jirousek-0598807.pdf https://link.springer.com/chapter/10.1007/978-3-031-65993-5_23 GA21-07494S GA ČR CZ cav_un_auth*0430801 The paper deals with the problem studied in our previous paper published in Int. J. Approx. Reasoning, which raised new questions rather than brought solutions. Thus, the current contribution also tries to answer the ever-lasting question: Which belief function entropies described in the literature can detect optimal models? Nevertheless, here, we approach the problem differently. We try to find out the entropy functions that are indirectly proportional to the informative content of belief functions, i.e., the moreinformative the belief function, the lower its entropy. cav_un_auth*0472961 International Conference on Soft Methods in Probability and Statistics 2024 - SMPS 2024 /11./ 20240903 20240906 Salzburg AT BA 10000 10100 10102 2025 2 PR RVO:67985556 https://hdl.handle.net/11104/0356718 S 2024 SCOPUS PUBMED 001323540900023 WOS cav_un_epca*0598452 Combining, Modelling and Analyzing Imprecision, Randomness and Dependence Springer 2024 Cham 192 199 978-3-031-65992-8 Advances in Intelligent Systems and Computing 2194-5357