Evidential-EM Algorithm Applied to Progressively Censored Observations

Kuang Zhou (1, 2) , Arnaud Martin (2) , Quan Pan (1)

1 School of Automation
2 DRUID - Declarative & Reliable management of Uncertain, user-generated Interlinked Data

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 incom-plete 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 infor-mation with the current imprecise knowledge conveyed by the observed data.

Mots clés: Belief function theory; Evidential-EM; Mixed-distribution; Uncertainty; Reliability analysis

Domaines: Intelligence artificielle [cs.AI]

Kuang Zhou, Arnaud Martin, Quan Pan. Evidential-EM Algorithm Applied to Progressively Censored Observations. 15th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jul 2014, Montpellier, France. pp.180 - 189, ⟨10.1007/978-3-319-08852-5_19⟩. ⟨hal-01100840⟩