Evidential-EM Algorithm Applied to Progressively Censored Observations

  • Kuang Zhou
  • Arnaud Martin
  • Quan Pan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 444)


Evidential-EM (E2M) algorithm is an effective approach for computing maximum likelihood estimations under finite mixture models, especially when there is uncertain information about data. In this paper we present an extension of the E2M method in a particular case of incomplete data, where the loss of information is due to both mixture models and censored observations. The prior uncertain information is expressed by belief functions, while the pseudo-likelihood function is derived based on imprecise observations and prior knowledge. Then E2M method is evoked to maximize the generalized likelihood function to obtain the optimal estimation of parameters. Numerical examples show that the proposed method could effectively integrate the uncertain prior information with the current imprecise knowledge conveyed by the observed data.


Belief function theory Evidential-EM Mixed-distribution Uncertainty Reliability analysis 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kuang Zhou
    • 1
    • 2
  • Arnaud Martin
    • 2
  • Quan Pan
    • 1
  1. 1.School of AutomationNorthwestern Polytechnical UniversityXi’anP.R. China
  2. 2.IRISA, University of Rennes 1LannionFrance

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