Original title: Computing the Decomposable Entropy of Graphical Belief Function Models
Authors: Jiroušek, Radim ; Kratochvíl, Václav ; Shenoy, P. P.
Document type: Papers
Conference/Event: WUPES 2022: 12th Workshop on Uncertainty Processing, Kutná Hora (CZ), 20220601
Year: 2022
Language: eng
Abstract: In 2018, Jiroušek and Shenoy proposed a definition of entropy for Dempster-Shafer (D-S) belief functions called decomposable entropy. Here, we provide an algorithm for computing the decomposable entropy of directed graphical D-S belief function models. For undirected graphical belief function models, assuming that each belief function in the model is non-informative to the others, no algorithm is necessary. We compute the entropy of each belief function and add them together to get the decomposable entropy of the model. Finally, the decomposable entropy generalizes Shannon’s entropy not only for the probability of a single random variable but also for multinomial distributions expressed as directed acyclic graphical models called Bayesian networks.
Keywords: Bayesian networks; Decomposable Entropy; DempsterShafer belief functions
Project no.: GA19-04579S (CEP), GA19-06569S (CEP)
Funding provider: GA ČR, GA ČR
Host item entry: Proceedings of the 12th Workshop on Uncertainty Processing, ISBN 978-80-7378-460-7

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2022/MTR/kratochvil-0558135.pdf
Original record: http://hdl.handle.net/11104/0332321

Permalink: http://www.nusl.cz/ntk/nusl-508629


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