کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
808450 905702 2007 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Formulating informative, data-based priors for failure probability estimation in reliability analysis
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی مکانیک
پیش نمایش صفحه اول مقاله
Formulating informative, data-based priors for failure probability estimation in reliability analysis
چکیده انگلیسی

Priors play an important role in the use of Bayesian methods in risk analysis, and using all available information to formulate an informative prior can lead to more accurate posterior inferences. This paper examines the practical implications of using five different methods for formulating an informative prior for a failure probability based on past data. These methods are the method of moments, maximum likelihood (ML) estimation, maximum entropy estimation, starting from a non-informative ‘pre-prior’, and fitting a prior based on confidence/credible interval matching. The priors resulting from the use of these different methods are compared qualitatively, and the posteriors are compared quantitatively based on a number of different scenarios of observed data used to update the priors. The results show that the amount of information assumed in the prior makes a critical difference in the accuracy of the posterior inferences. For situations in which the data used to formulate the informative prior is an accurate reflection of the data that is later observed, the ML approach yields the minimum variance posterior. However, the maximum entropy approach is more robust to differences between the data used to formulate the prior and the observed data because it maximizes the uncertainty in the prior subject to the constraints imposed by the past data.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Reliability Engineering & System Safety - Volume 92, Issue 4, April 2007, Pages 490–502
نویسندگان
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