Computing the Decomposable Entropy of Graphical Belief Function Models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F22%3A00558135" target="_blank" >RIV/67985556:_____/22:00558135 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Computing the Decomposable Entropy of Graphical Belief Function Models
Original language description
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.
Czech name
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Czech description
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Project
<a href="/en/project/GA19-04579S" target="_blank" >GA19-04579S: Conditonal independence structures: methods of polyhedral geometry</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 12th Workshop on Uncertainty Processing
ISBN
978-80-7378-460-7
ISSN
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e-ISSN
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Number of pages
12
Pages from-to
111-122
Publisher name
MatfyzPress
Place of publication
Prague
Event location
Kutná Hora
Event date
Jun 1, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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