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Computing the decomposable entropy of belief-function graphical models
dc.contributor.author | Jiroušek, Radim | |
dc.contributor.author | Kratochvíl, Václav | |
dc.contributor.author | Shenoy, Prakash P. | |
dc.date.accessioned | 2023-08-10T15:22:55Z | |
dc.date.available | 2023-08-10T15:22:55Z | |
dc.date.issued | 2023-07-13 | |
dc.identifier.citation | Jiroušek, R., Kratochvíl, V., Shenoy, P.P., (2023), Computing the decomposable entropy of belief-function graphical models, International Journal of Approximate Reasoning, vol. 161, 108984, https://doi.org/10.1016/j.ijar.2023.108984 | en_US |
dc.identifier.uri | https://hdl.handle.net/1808/34711 | |
dc.description.abstract | 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. | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | © 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license. | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
dc.subject | Dempster-Shafer theory of belief functions | en_US |
dc.subject | Decomposable entropy | en_US |
dc.subject | Belief-function directed graphical models | en_US |
dc.subject | Belief-function undirected graphical models | en_US |
dc.title | Computing the decomposable entropy of belief-function graphical models | en_US |
dc.type | Article | en_US |
kusw.kuauthor | Shenoy, Prakash P. | |
kusw.kudepartment | Business | en_US |
dc.identifier.doi | 10.1016/j.ijar.2023.108984 | en_US |
dc.identifier.orcid | https://orcid.org/0000-0002-8425-896X | en_US |
kusw.oaversion | Scholarly/refereed, publisher version | en_US |
kusw.oapolicy | This item meets KU Open Access policy criteria. | en_US |
dc.rights.accessrights | openAccess | en_US |