Likelihood Analysis of Imperfect Data
Author:
Affiliation:
1. Alliance Manchester Business School, The University of Manchester, Manchester, U.K
2. Department of Automation, Hangzhou Dianzi University, Hangzhou, China
3. School of Management, Hefei University of Technology, Hefei, China
Funder
Alliance Strategic Research Fund of The University of Manchester
European Union Horizon 2020 Research and Innovation Programme RISE
U.S. Air Force Office of Scientific Research
Natural Science Foundation of China
InnoHK initiative, the Government of the HKSAR
Hong Kong Laboratory for AI-Powered Financial Technologies
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Control and Systems Engineering,Software
Link
http://xplorestaging.ieee.org/ielx7/6221021/10186109/10097762.pdf?arnumber=10097762
Reference47 articles.
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2. Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem
3. Maximum likelihood from incomplete data via the EM algorithm;dempster;J Roy Statist Soc B Statist Methodol,1977
4. Fusion of sources of evidence with different importances and reliabilities
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