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A novel Bayesian approach to reliability modeling: The benefits of uncertainty evaluation in the model selection procedure
Author(s) -
Santana D. D.,
Figueirôa Filho C. L. S.,
Sartori I.,
Martins Márcio A. F.
Publication year - 2018
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2312
Subject(s) - model selection , reliability (semiconductor) , bayesian probability , selection (genetic algorithm) , computer science , bayesian inference , goodness of fit , machine learning , data mining , econometrics , artificial intelligence , mathematics , power (physics) , physics , quantum mechanics
Abstract This paper proposes a different likelihood formulation within the Bayesian paradigm for parameter estimation of reliability models. Moreover, the assessment of the uncertainties associated with parameters, the goodness of fit, and the model prediction of reliability are included in a systematic framework for better aiding the model selection procedure. Two case studies are appraised to highlight the contributions of the proposed method and demonstrate the differences between the proposed Bayesian formulation and an existing Bayesian formulation.