TY - JOUR
T1 - Parameter estimation based on interval-valued belief structures
AU - Deng, Xinyang
AU - Hu, Yong
AU - Chan, Felix T.S.
AU - Mahadevan, Sankaran
AU - Deng, Yong
N1 - Publisher Copyright:
© 2014 Elsevier B.V. All rights reserved.
PY - 2015/3/1
Y1 - 2015/3/1
N2 - Parameter estimation based on uncertain data represented as belief structures is one of the latest problems in the Dempster-Shafer theory. In this paper, a novel method is proposed for the parameter estimation in the case where belief structures are uncertain and represented as interval-valued belief structures. Within our proposed method, the maximization of likelihood criterion and minimization of estimated parameter's uncertainty are taken into consideration simultaneously. As an illustration, the proposed method is employed to estimate parameters for deterministic and uncertain belief structures, which demonstrates its effectiveness and versatility.
AB - Parameter estimation based on uncertain data represented as belief structures is one of the latest problems in the Dempster-Shafer theory. In this paper, a novel method is proposed for the parameter estimation in the case where belief structures are uncertain and represented as interval-valued belief structures. Within our proposed method, the maximization of likelihood criterion and minimization of estimated parameter's uncertainty are taken into consideration simultaneously. As an illustration, the proposed method is employed to estimate parameters for deterministic and uncertain belief structures, which demonstrates its effectiveness and versatility.
KW - Belief function
KW - Dempster-Shafer theory
KW - Interval-valued belief structures
KW - Maximum likelihood estimation
KW - Parameter estimation
UR - http://www.scopus.com/inward/record.url?scp=84910143958&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2014.10.002
DO - 10.1016/j.ejor.2014.10.002
M3 - 文章
AN - SCOPUS:84910143958
SN - 0377-2217
VL - 241
SP - 579
EP - 582
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 2
ER -