DocumentCode
526005
Title
Notice of Retraction
Sampling stopping procedure of stochastic simulation for Dynamic-Stochastic Influence Diagrams evaluation
Author
Zhao Xin ; Zhu Yi-fan ; Li Qun
Author_Institution
Inst. of Syst. Eng., Nat. Univ. of Defense Technol., Changsha, China
Volume
3
fYear
2010
fDate
12-13 June 2010
Firstpage
124
Lastpage
127
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The Influence Diagrams (IDs) is a kind of Probabilistic Belief Networks for graphic modeling. The usage of IDs can improve the communication among field experts, modelers, and decisionmakers, by showing the issue frame from a high-level point of view. But with the size and scope increasing of the complex issue descibed, the IDs model evaluation is more and more difficult. Moreover, the appearances of some extended IDs made it´s crying for approximate evaluating methods. This paper studys stochastic simulation for the evaluation of Dynamic-Stochastic Influence Diagrams (DSIDs), a kind of extended IDs, to discuss its sampling stopping procedure. The introduced stopping procedure and its implementation for scenarios comparison could bring more exact sampling times or less sampling time.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The Influence Diagrams (IDs) is a kind of Probabilistic Belief Networks for graphic modeling. The usage of IDs can improve the communication among field experts, modelers, and decisionmakers, by showing the issue frame from a high-level point of view. But with the size and scope increasing of the complex issue descibed, the IDs model evaluation is more and more difficult. Moreover, the appearances of some extended IDs made it´s crying for approximate evaluating methods. This paper studys stochastic simulation for the evaluation of Dynamic-Stochastic Influence Diagrams (DSIDs), a kind of extended IDs, to discuss its sampling stopping procedure. The introduced stopping procedure and its implementation for scenarios comparison could bring more exact sampling times or less sampling time.
Keywords
belief networks; sampling methods; stochastic processes; dynamic-stochastic influence diagrams; probabilistic belief networks; sampling stopping procedure; stochastic simulation; Computational modeling; Computers; Europe; Variable speed drives; dynamic-stochastic influence diagrams; influence diagrams evaluation; sampling stopping procedure; stochastic simulation;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Communication Technologies in Agriculture Engineering (CCTAE), 2010 International Conference On
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6944-4
Type
conf
DOI
10.1109/CCTAE.2010.5544377
Filename
5544377
Link To Document