| bibtype |
J -
Journal Article
|
| ARLID |
0523947 |
| utime |
20240103224006.2 |
| mtime |
20200424235959.9 |
| WOS |
000497900500001 |
| SCOPUS |
85075433795 |
| DOI |
10.1080/03081079.2019.1692006 |
| title
(primary) (eng) |
A Note on Approximation of Shenoy's Expectation Operator Using Probabilistic Transforms |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0256794 |
| ISSN |
0308-1079 |
| title
|
International Journal of General Systems |
| volume_id |
49 |
| volume |
1 (2020) |
| page_num |
48-63 |
| publisher |
|
|
| keyword |
Expectation |
| keyword |
belief function |
| keyword |
probabilistic transform |
| keyword |
commonality function |
| keyword |
utility |
| keyword |
ambiguity |
| keyword |
Choquet integral |
| author
(primary) |
| ARLID |
cav_un_auth*0101118 |
| full_dept |
Department of Decision Making Theory |
| share |
34 |
| name1 |
Jiroušek |
| name2 |
Radim |
| institution |
UTIA-B |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept (eng) |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department (eng) |
MTR |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0216188 |
| full_dept |
Department of Decision Making Theory |
| share |
33 |
| name1 |
Kratochvíl |
| name2 |
Václav |
| institution |
UTIA-B |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department |
MTR |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0274184 |
| share |
33 |
| name1 |
Rauh |
| name2 |
J. |
| country |
DE |
|
| source |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0379647 |
| project_id |
GA19-06569S |
| agency |
GA ČR |
| country |
CZ |
|
| abstract
(eng) |
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 needed\nthe 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. |
| result_subspec |
WOS |
| RIV |
IN |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2021 |
| num_of_auth |
3 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0308327 |
| confidential |
S |
| mrcbC91 |
C |
| mrcbT16-e |
COMPUTERSCIENCETHEORYMETHODS |
| mrcbT16-i |
0.20849 |
| mrcbT16-j |
0.43 |
| mrcbT16-s |
0.482 |
| mrcbT16-B |
44.296 |
| mrcbT16-D |
Q3 |
| mrcbT16-E |
Q3 |
| arlyear |
2020 |
| mrcbU14 |
85075433795 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000497900500001 WOS |
| mrcbU63 |
cav_un_epca*0256794 International Journal of General Systems 0308-1079 1563-5104 Roč. 49 č. 1 2020 48 63 Taylor & Francis |
|