可靠性应用中指定先验分布的讨论

IF 1.3 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Frank P.A. Coolen
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引用次数: 0

摘要

论文《在可靠性应用中指定先验分布》主要概述了为基本寿命分布参数选择非信息先验分布的方法,这些参数经常用于可靠性分析。该讨论提出了一些相关问题,并对基本贝叶斯统计方法以外的可能在可靠性应用中有用的机会进行了评论。讨论的主要重点是可用数据较少的实际可靠性分析,在这种情况下往往需要信息先验而不是非信息先验,以便将专家的判断考虑在内。此外,虽然对先验分布非信息性的抽象考虑具有理论意义,但在大多数实际情况下,我们的目标是决策支持,应考虑假定先验对最终决策的影响,理想情况下,最终决策对所有被认为合理的先验都具有稳健性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discussion of specifying prior distributions in reliability applications

The paper Specifying Prior Distributions in Reliability Applications mainly provides an overview of methods for selecting non-informative prior distributions for parameters of basic lifetime distributions, as often used in reliability analyses. This discussion raises some related issues and comments on opportunities beyond basic Bayesian statistical methods which may be useful in reliability scenarios. The main emphasis in this discussion is on practical reliability analyses with few data available, where there is often need for informative priors rather than for non-informative priors, in order to take expert judgement into account. Furthermore, while rather abstract considerations of non-informativeness of prior distributions is of theoretic interest, in most practical scenarios one aims at decision support, and the influence of assumed priors on the final decisions should be considered, ideally with robustness of the final decision with regard to all priors which are deemed to be reasonable.

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来源期刊
CiteScore
2.70
自引率
0.00%
发文量
67
审稿时长
>12 weeks
期刊介绍: ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.
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