{"title":"Bayesian modeling and inference for one-shot experiments","authors":"J. Rougier, Andrew Duncan","doi":"10.1080/00401706.2023.2224524","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":" ","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technometrics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/00401706.2023.2224524","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
期刊介绍:
Technometrics is a Journal of Statistics for the Physical, Chemical, and Engineering Sciences, and is published Quarterly by the American Society for Quality and the American Statistical Association.Since its inception in 1959, the mission of Technometrics has been to contribute to the development and use of statistical methods in the physical, chemical, and engineering sciences.