A Note on Approximation of Shenoy's Expectation Operator Using Probabilistic Transforms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F20%3A00523947" target="_blank" >RIV/67985556:_____/20:00523947 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.tandfonline.com/doi/full/10.1080/03081079.2019.1692006" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/03081079.2019.1692006</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/03081079.2019.1692006" target="_blank" >10.1080/03081079.2019.1692006</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Note on Approximation of Shenoy's Expectation Operator Using Probabilistic Transforms

Popis výsledku v původním jazyce

Recently, a new way of computing an expected value in the Dempster-Shafer theory of evidence was introduced by Prakash P. Shenoy. Up to now, when they needednthe expected value of a utility function in D-S theory, the authors usually did it indirectly: first, they found a probability measure corresponding to the considered belief function, and then computed the classical probabilistic expectation using this probability measure. To the best of our knowledge, Shenoy's operator of expectation is the first approach that takes into account all the information included in the respective belief function. Its only drawback is its exponential computational complexity. This is why, in this paper, we compare five different approaches defining probabilistic representatives of belief function from the point of view, which of them yields the best approximations of Shenoy's expected values of utility functions.

Název v anglickém jazyce

A Note on Approximation of Shenoy's Expectation Operator Using Probabilistic Transforms

Popis výsledku anglicky

Recently, a new way of computing an expected value in the Dempster-Shafer theory of evidence was introduced by Prakash P. Shenoy. Up to now, when they needednthe expected value of a utility function in D-S theory, the authors usually did it indirectly: first, they found a probability measure corresponding to the considered belief function, and then computed the classical probabilistic expectation using this probability measure. To the best of our knowledge, Shenoy's operator of expectation is the first approach that takes into account all the information included in the respective belief function. Its only drawback is its exponential computational complexity. This is why, in this paper, we compare five different approaches defining probabilistic representatives of belief function from the point of view, which of them yields the best approximations of Shenoy's expected values of utility functions.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of General Systems

ISSN

0308-1079

e-ISSN

—

Svazek periodika

49

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

16

Strana od-do

48-63

Kód UT WoS článku

000497900500001

EID výsledku v databázi Scopus

2-s2.0-85075433795