{"title":"确定三角分布和PERT分布的准确性","authors":"Imran A. Khan, J. Bickel, Robert K. Hammond","doi":"10.1287/deca.2022.0464","DOIUrl":null,"url":null,"abstract":"The Triangular and PERT (Program Evaluation Review Technique) distribution probability density functions are commonly used in decision and risk analyses. These distributions are popular because they are each specified by only three points (two support bounds and the mode) that are believed to be easy to assess from experts or data. In this paper, we carefully analyze how close the Triangular and PERT distributions are to other distributions sharing the same support and mode and show that the errors induced by the Triangular and PERT distributions are significant. We further show that distributions that are characterized by the median tend to provide a better fit than do those that are characterized by the mode. Funding: This research was supported by the Equinor Fellows Program and the Operating System 2.0 research program developed by the Construction Industry Institute.","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Determining the Accuracy of the Triangular and PERT Distributions\",\"authors\":\"Imran A. Khan, J. Bickel, Robert K. Hammond\",\"doi\":\"10.1287/deca.2022.0464\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Triangular and PERT (Program Evaluation Review Technique) distribution probability density functions are commonly used in decision and risk analyses. These distributions are popular because they are each specified by only three points (two support bounds and the mode) that are believed to be easy to assess from experts or data. In this paper, we carefully analyze how close the Triangular and PERT distributions are to other distributions sharing the same support and mode and show that the errors induced by the Triangular and PERT distributions are significant. We further show that distributions that are characterized by the median tend to provide a better fit than do those that are characterized by the mode. Funding: This research was supported by the Equinor Fellows Program and the Operating System 2.0 research program developed by the Construction Industry Institute.\",\"PeriodicalId\":46460,\"journal\":{\"name\":\"Decision Analysis\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2023-01-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Decision Analysis\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1287/deca.2022.0464\",\"RegionNum\":4,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Decision Analysis","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1287/deca.2022.0464","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MANAGEMENT","Score":null,"Total":0}
Determining the Accuracy of the Triangular and PERT Distributions
The Triangular and PERT (Program Evaluation Review Technique) distribution probability density functions are commonly used in decision and risk analyses. These distributions are popular because they are each specified by only three points (two support bounds and the mode) that are believed to be easy to assess from experts or data. In this paper, we carefully analyze how close the Triangular and PERT distributions are to other distributions sharing the same support and mode and show that the errors induced by the Triangular and PERT distributions are significant. We further show that distributions that are characterized by the median tend to provide a better fit than do those that are characterized by the mode. Funding: This research was supported by the Equinor Fellows Program and the Operating System 2.0 research program developed by the Construction Industry Institute.