0573803 20240402214200.620230724235959.9 85165544597 001058204600001 10.1016/j.ijar.2023.108984 21 s. P cav_un_epca*02567740888-613X161Elsevier Dempster-Shafer theory of belief functions Decomposable entropy Belief-function directed graphical models Belief-function undirected graphical models 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/2023/MTR/jirousek-0573803.pdf https://www.sciencedirect.com/science/article/pii/S0888613X23001159?via%3Dihub GA21-07494S GA ČR CZ cav_un_auth*0430801 In 2018, Jiroušek and Shenoy proposed a definition of entropy for Dempster-Shafer (D-S) belief functions called decomposable entropy (d-entropy). This paper provides an algorithm for computing the d-entropy of directed graphical D-S belief function models. We illustrate the algorithm using Almond's Captain's Problem example. For belief function undirected graphical models, assuming that the set of belief functions in the model is non-informative, the belief functions are distinct. We illustrate this using Haenni-Lehmann's Communication Network problem. As the joint belief function for this model is quasi-consonant, it follows from a property of d-entropy that the d-entropy of this model is zero, and no algorithm is required. For a class of undirected graphical models, we provide an algorithm for computing the d-entropy of such models. Finally, the d-entropy coincides with Shannon's entropy for the probability mass function of a single random variable and for a large multi-dimensional probability distribution expressed as a directed acyclic graph model called a Bayesian network. We illustrate this using Lauritzen-Spiegelhalter's Chest Clinic example represented as a belief-function directed graphical model. cav_un_auth*0452419 The 12th Workshop on Uncertainty Processing 20220601 20220604 Kutná Hora CZ WOS BA 10000 10100 10102 2024 3 RVO:67985556 https://hdl.handle.net/11104/0344420 S 108984 Article Computer Science Artificial Intelligence A COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE 3.2 0.7 6.3 0.00458 0.75 5066 116 0.877 47.31 3.74 1655 Q1 2.600 155 Q2 57.6 Q3 Q2 1.02 Q2 57.6 2023 85165544597 SCOPUS PUBMED 001058204600001 WOS cav_un_epca*0256774 International Journal of Approximate Reasoning 161 1 2023 0888-613X 1873-4731 Elsevier