A Bayesian analysis of industrial lifetime data with Weibull distributions

Nicolas Bousquet 1
1 SELECT - Model selection in statistical learning
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay, CNRS - Centre National de la Recherche Scientifique : UMR
Abstract : The context of our study is industrial reliability, where lifetime data are usually censored and in small number. Background information is available from experts. Our prior subjective knowledge is only about the lifetime of an industrial component and not about the parameters of a Weibull distribution which represents this lifetime. We propose to focus the discussion between the experts and the industrial analyst about the size of {\it virtual} data representing the variability of the expert opinion. Indeed, this size is one of the scarce indicators that both can understand. The prior calibration is made easy, and some methods and indicators including a default calibration method are proposed to help the Bayesian analyst (they can be extended to inferences on other distributions than Weibull). Besides, the posterior computation by importance sampling is simple and satisfying. Finally, through a real example, the flexibility of the elicitation is illustrated.
Type de document :
[Research Report] RR-6025, INRIA. 2006, pp.24
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Contributeur : Rapport de Recherche Inria <>
Soumis le : jeudi 23 novembre 2006 - 09:48:53
Dernière modification le : jeudi 11 janvier 2018 - 01:49:37
Document(s) archivé(s) le : vendredi 24 septembre 2010 - 10:44:34


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  • HAL Id : inria-00115528, version 4



Nicolas Bousquet. A Bayesian analysis of industrial lifetime data with Weibull distributions. [Research Report] RR-6025, INRIA. 2006, pp.24. 〈inria-00115528v4〉



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