0636593 20260226095259.620250616235959.9 12 s. P cav_un_epca*0636591978-80-7378-525-392-103PragueMatfyzPress2025StudenýMilanAyNihatCapotortiAndreaCsirmazLászlóJiroušekRadimKleiterGernot D.ShenoyPrakash P. belief function learning confidence interval cav_un_auth*0100740 Daniel Milan UIVT-O Oddělení složitých systémů Department of Complex Systems Ústav informatiky AV ČR, v. v. i. 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. http://library.utia.cas.cz/separaty/2025/MTR/jirousek-0636593.pdf Not all normalized nonnegative monotone set functions are belief functions. This paper investigates ways to modify them to obtain a belief function that preserves some of their properties. The problem is motivated by an approach to data-based learning of belief function models. The approach is based on the idea that classical methods of mathematical statistics can provide estimates of lower bounds for unknown probabilities. Thus, methods of mathematical statistics can be used to obtain a reasonable rough estimate, which is further elaborated to obtain a desired belief function model. cav_un_auth*0489177 Workshop on Uncertainty Processing - WUPES 2025 /13./ 20250604 20250607 Třešť CZ BA 10000 10100 10102 2026 3 UIVT-O 10000 10200 10201 PR RVO:67985556 RVO:67985807 https://hdl.handle.net/11104/0367706 S 2025 SCOPUS PUBMED WOS pdf cav_un_epca*0636591 Proceedings of the 13th Workshop on Uncertainty Processing (WUPES’25) 978-80-7378-525-3 92 103 Prague MatfyzPress 2025 719 Studený Milan 340 Ay Nihat 340 Capotorti Andrea 340 Csirmaz László 340 Jiroušek Radim 340 Kleiter Gernot D. 340 Shenoy Prakash P. 340