A kind of data repairing for missing data of discrete dynamic Bayesian networks forwards information repairing algorithm

Haiyang Chen, Xiaoguang Gao, Jingsong Zheng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The study about static Bayesian networks with missing data has got some mature algorithms. Because of the complexity of Dynamic Bayesian Networks (DBNs), the mature methods can't be directly extended to dynamic systems. We proposed a kind of data repairing for the missing data of Discrete Dynamic Bayesian Networks (DDBNs). It incorporated network parameters, evidences and dynamic networks to predict the missing data. It's also an online repairing algorithm. Repairing algorithm was deduced in theory and verified by examples. Repaired networks make the target track and identification ability stronger. It can greatly improve the accuracy and reliability of the identification systems.

Original languageEnglish
Title of host publication5th International Conference on Natural Computation, ICNC 2009
Pages47-51
Number of pages5
DOIs
StatePublished - 2009
Event5th International Conference on Natural Computation, ICNC 2009 - Tianjian, China
Duration: 14 Aug 200916 Aug 2009

Publication series

Name5th International Conference on Natural Computation, ICNC 2009
Volume6

Conference

Conference5th International Conference on Natural Computation, ICNC 2009
Country/TerritoryChina
CityTianjian
Period14/08/0916/08/09

Keywords

  • Data completion
  • Discrete dynamic bayesian networks
  • Missing at random
  • Predication
  • Target recognition

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