{"title":"基于先验知识的A类不确定度贝叶斯评价方法的分析与比较","authors":"I. Lira","doi":"10.24027/2306-7039.4.2022.276284","DOIUrl":null,"url":null,"abstract":"If a number of observations about a certain quantity may be assumed independent, drawn from a Gaussian distribution, Supplement 1 to the GUM recommends that the standard uncertainty associated with the quantity be obtained by a formula that is applied to more than three observations. Various articles have recently appeared proposing Bayesian methods to surmount this limitation. Some of these methods, which require prior knowledge about the quantity, are reviewed in this article.","PeriodicalId":40775,"journal":{"name":"Ukrainian Metrological Journal","volume":null,"pages":null},"PeriodicalIF":0.1000,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis and comparison of Bayesian methods for type A uncertainty evaluation with prior knowledge\",\"authors\":\"I. Lira\",\"doi\":\"10.24027/2306-7039.4.2022.276284\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"If a number of observations about a certain quantity may be assumed independent, drawn from a Gaussian distribution, Supplement 1 to the GUM recommends that the standard uncertainty associated with the quantity be obtained by a formula that is applied to more than three observations. Various articles have recently appeared proposing Bayesian methods to surmount this limitation. Some of these methods, which require prior knowledge about the quantity, are reviewed in this article.\",\"PeriodicalId\":40775,\"journal\":{\"name\":\"Ukrainian Metrological Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.1000,\"publicationDate\":\"2022-12-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ukrainian Metrological Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.24027/2306-7039.4.2022.276284\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"INSTRUMENTS & INSTRUMENTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ukrainian Metrological Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24027/2306-7039.4.2022.276284","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
Analysis and comparison of Bayesian methods for type A uncertainty evaluation with prior knowledge
If a number of observations about a certain quantity may be assumed independent, drawn from a Gaussian distribution, Supplement 1 to the GUM recommends that the standard uncertainty associated with the quantity be obtained by a formula that is applied to more than three observations. Various articles have recently appeared proposing Bayesian methods to surmount this limitation. Some of these methods, which require prior knowledge about the quantity, are reviewed in this article.