.png){kind=icon}
返回
Bayesian methodology for reliability model acceptance
.png){kind=icon}
PRE
AI
DOI：10.1016/S0951-8320(02)00269-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper develops a methodology to assess the reliability computation model validity using the concept of Bayesian hypothesis testing, by comparing the model prediction and experimental observation, when there is only one computational model available to evaluate system behavior. Time-independent and time-dependent problems are investigated, with consideration of both cases: with and without statistical uncertainty in the model. The case of time-independent failure probability prediction with no statistical uncertainty is a straightforward application of Bayesian hypothesis testing. However, for the life prediction (time-dependent reliability) problem, a new methodology is developed in this paper to make the same Bayesian hypothesis testing concept applicable. With the existence of statistical uncertainty in the model, in addition to the application of a predictor estimator of the Bayes factor, the uncertainty in the Bayes factor is explicitly quantified through treating it as a random variable and calculating the probability that it exceeds a specified value. The developed method provides a rational criterion to decision-makers for the acceptance or rejection of the computational model. (C) 2003 Elsevier Science Ltd. All rights reserved.
Keyword:
model uncertainty
hypothesis testing
Bayesian statistics
Bayes factor
model testing
model validation
statistical uncertainty
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
R
IF:
11
论文数:
9.0K
被引数:
4.2W
机构
暂无机构信息
引用论文
Predictors of Upper Extremity Discomfort: A Longitudinal Study of Industrial and Clerical Workers
.png){kind=icon}
PREAI
分享收藏Low energy ion assist during deposition — an effective tool for controlling thin film microstructure
PREAI
分享收藏分享收藏
没有更多内容
没有更多内容