0558137 20230316105120.120220613235959.9 12 s. P cav_un_epca*0558134978-80-7378-460-7123-134PragueMatfyzPress2022StudenýMilanAyNihatColettiGiulianellaKleiterGernot D.ShenoyPrakash P. probability theory Bayesian networks belief functions 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. cav_un_auth*0275452 Shenoy P. P. US http://library.utia.cas.cz/separaty/2022/MTR/kratochvil-0558137.pdf GA19-06569S GA ČR CZ cav_un_auth*0380559 In probability theory, compositional models are as powerful as Bayesian networks. However, the relation between belief-function graphical models and the corresponding compositional models is much more complicated due to several reasons. One of them is that there are two composition operators for belief functions. This paper deals with their main properties and presents sufficient conditions under which they yield the same results. cav_un_auth*0431432 WUPES 2022: 12th Workshop on Uncertainty Processing 20220601 20220604 Kutná Hora CZ BA 10000 10100 10102 2023 3 PR RVO:67985556 http://hdl.handle.net/11104/0332322 S 2022 SCOPUS PUBMED WOS cav_un_epca*0558134 Proceedings of the 12th Workshop on Uncertainty Processing MatfyzPress 2022 Prague 123 134 978-80-7378-460-7 Studený Milan 340 Ay Nihat 340 Coletti Giulianella 340 Kleiter Gernot D. 340 Shenoy Prakash P. 340