0546760 20220320214454.220211018235959.9 10.1007/978-3-030-88601-1_12 10 s. P cav_un_epca*0546759978-3-030-88600-40302-9743117-126ChamSpringer2021DenœuxT.LefèvreE.LiuZ.PichonF. Compositional models Entropy of Dempster-Shafer belief functions Decomposable entropy of Dempster-Shafer 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/2021/MTR/jirousek-0546760.pdf GA19-06569S GA ČR CZ cav_un_auth*0380559 We investigate learning of belief function compositional models from data using information content and mutual information based on two different definitions of entropy proposed by Jiroušek and Shenoy in 2018 and 2020, respectively. The data consists of 2,310 randomly generated basic assignments of 26 binary variables from a pairwise consistent and decomposable compositional model. We describe results achieved by three simple greedy algorithms for constructing compositional models from the randomly generated low-dimensional basic assignments. cav_un_auth*0415505 International Conference on Belief Functions 2021 /6./ 20211015 20211019 Shanghai CN BA 10000 10100 10101 2022 3 PR RVO:67985556 http://hdl.handle.net/11104/0323760 S 499 0.427 21.58 1.08 84158 Q2 2021 SCOPUS PUBMED WOS cav_un_epca*0546759 Belief Functions: Theory and Applications - 6th International Conference, BELIEF 2021 - Proceedings 978-3-030-88600-4 0302-9743 1611-3349 117 126 Cham Springer 2021 1 Lecture Notes in Computer Science 12915 Denœux T. 340 Lefèvre E. 340 Liu Z. 340 Pichon F. 340