{"title":"Discussion of specifying prior distributions in reliability applications—Applications for Bayesian estimation software design","authors":"Peng Liu","doi":"10.1002/asmb.2786","DOIUrl":null,"url":null,"abstract":"<p>It is a great pleasure to have the opportunity to write a discussion on “Specifying Prior Distributions in Reliability Applications” by Tian et al. Appl Stochast Models Bus Ind, (2023). One coauthor of the paper, Dr Meeker, has conducted Bayesian methodology research on reliability data analysis for many years, and I have followed his work on the subject for quite some time. The work by Dr Meeker helped us develop Bayesian estimation products which are both powerful and easy to use. This time, I learned something new as usual. In this discussion, I will focus on the great value of the paper for developing user friendly Bayesian estimation software.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"40 1","pages":"79-81"},"PeriodicalIF":1.3000,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Stochastic Models in Business and Industry","FirstCategoryId":"100","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/asmb.2786","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
It is a great pleasure to have the opportunity to write a discussion on “Specifying Prior Distributions in Reliability Applications” by Tian et al. Appl Stochast Models Bus Ind, (2023). One coauthor of the paper, Dr Meeker, has conducted Bayesian methodology research on reliability data analysis for many years, and I have followed his work on the subject for quite some time. The work by Dr Meeker helped us develop Bayesian estimation products which are both powerful and easy to use. This time, I learned something new as usual. In this discussion, I will focus on the great value of the paper for developing user friendly Bayesian estimation software.
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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