0387929 20240103202030.220130207235959.9 000312969600046 10.1007/978-3-642-33042-1_46 8 s. P cav_un_epca*0387928978-3-642-33041-4429-436HeidelbergSpringer2012 evidence theory conditioning conditional independence evidential networks cav_un_auth*0101223 Vejnarová Jiřina Matematická teorie rozhodování Department of Decision Making Theory MTR MTR UTIA-B Department of Decision Making Theory Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2013/MTR/vejnarova-evidential networks from a different perspective.pdf GAP402/11/0378 GA ČR cav_un_auth*0273630 Bayesian networks are, at present, probably the most popular representative of so-called graphical Markov models. Naturally, several attempts to construct an analogy of Bayesian networks have also been made in other frameworks as e.g. in possibility theory, evidence theory or in more general frameworks of valuation-based systems and credal sets. We collect previously obtained results concerning conditioning, conditional independence and irrelevance allowing to define a new type of evidential networks, based on conditional basic assignments. These networks can be seen as a generalization of Bayesian networks, however, they are less powerful than e.g. so-called compositional models, as we demonstrate by a simple example. cav_un_auth*0288302 Soft Methods In Probability and Statistics Konstanz 04.10.2012-06.10.2012 DE 2013 BA 1 PR RVO:67985556 http://hdl.handle.net/11104/0217945 2012 000312969600046 WOS cav_un_epca*0387928 Synergies of Soft Computing and Statistics for Intelligent Data Analysis 978-3-642-33041-4 429 436 Heidelberg Springer 2012 Advances in Intelligent Systems and Computing 190 Kruse R. 340 Berthold M. R. 340 Moewes Ch. 340 Gil M. A. 340 Grzegorzewski P. 340 Hryniewicz O. 340