0507137 20240103222344.820190801235959.9 85006014956 000389947600005 10.1007/978-3-319-49046-5_5 13 s. P cav_un_epca*0507136978-3-319-49045-80302-974351-63ChamSpringer2016HuynhVan-NamInuiguchiMasahiroLeBacLeBao NguyenDenoeuxThierry Sequential decision-making Multidimensional models Probability distributions Composition Information theory cav_un_auth*0101118 80 Jiroušek Radim UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory K Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0261750 Váchová L. CZ http://library.utia.cas.cz/separaty/2019/MTR/jirousek-0507137.pdf cav_un_auth*0353428 GA15-00215S GA ČR CZ The goal of the paper is to describe a classical sequential decision process that is often used for both medical and technical diagnosis making in a relatively new theoretical setting. For this, we represent the background knowledge, which is assumed to be expressed in the form of a multidimensional probability distribution, as a compositional model. cav_un_auth*0377840 5th International Symposium, IUKM 2016 20161130 20161202 Da Nang VN IN 10000 10200 10201 2020 2 PR RVO:67985556 http://hdl.handle.net/11104/0298538 S 3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Information Systems|Computer Science Theory Methods 0.325 Q2 2016 85006014956 SCOPUS PUBMED 000389947600005 WOS cav_un_epca*0507136 Integrated Uncertainty in Knowledge Modelling and Decision Making 978-3-319-49045-8 0302-9743 51 63 Integrated Uncertainty in Knowledge Modelling and Decision Making Cham Springer 2016 Lecture Notes in Artificial Intelligence LNAI 9978 340 Huynh Van-Nam 340 Inuiguchi Masahiro 340 Le Bac 340 Le Bao Nguyen 340 Denoeux Thierry