0555172 20230316104755.520220309235959.9 85126538632 000786448900001 10.1007/978-3-030-98018-4_1 12 s. P cav_un_epca*0555171978-3-030-98017-70302-97433-15ChamSpringer2022HondaKatsuhiroEntaniTomoeUbukataSeikiHuynhVan-NamInuiguchiMasahiro Belief functions Divergence Approximation Compositional models cav_un_auth*0101118 Jiroušek Radim UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory 50 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 50 Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2022/MTR/jirousek-0555172.pdf GA19-06569S GA ČR CZ cav_un_auth*0380559 Because of the high computational complexity of the respective procedures, the application of belief-function theory to problems of practice is possible only when the considered belief functions are approximated in an efficient way. Not all measures of similarity/dissimilarity are felicitous to measure the quality of such approximations. The paper presents results from a pilot study that tries to detect the divergences suitable for this purpose. cav_un_auth*0426364 International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making 2022 /9./ 20220318 20220319 Ishikawa JP BA 10000 10100 10101 2023 2 PR RVO:67985556 http://hdl.handle.net/11104/0330288 S 1 3+4 Proceedings Paper Computer Science Artificial Intelligence|Management 499 0.249 24.53 1.2 80471 Q3 2022 85126538632 SCOPUS PUBMED 000786448900001 WOS cav_un_epca*0555171 Integrated Uncertainty in Knowledge Modelling and Decision Making Springer 2022 Cham 3 15 978-3-030-98017-7 Lecture Notes in Computer Science 13199 0302-9743 1611-3349 Honda Katsuhiro 340 Entani Tomoe 340 Ubukata Seiki 340 Huynh Van-Nam 340 Inuiguchi Masahiro 340