{"title":"基于混合贝叶斯先验分布的可靠性验证测试模型","authors":"Feng Gao, Xiaoyun Zheng, Chang Liu","doi":"10.1109/CSO.2012.173","DOIUrl":null,"url":null,"abstract":"Using advantages of priori Bayesian method, a reliability verification test method based on Hybrid Bayesian Prior Distribution was brought forward. The prior distribution of unknown parameters can be obtained by using conjugate prior distribution method. Prior moment method and Maximum entropy method were used respectively to calculate two different groups of parameters, and then two different prior distributions can be obtained. Then confidence factors of the two prior distributions were determined by using the second category maximum likelihood method, and the final distribution can be got by integrating there two group of parameters according to their weight. Instance proved that the prior distribution obtained by this method is more accurate, and can fit better with the real distribution.","PeriodicalId":170543,"journal":{"name":"2012 Fifth International Joint Conference on Computational Sciences and Optimization","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Reliability Verification Test Model Based on Hybrid Bayesian Prior Distribution\",\"authors\":\"Feng Gao, Xiaoyun Zheng, Chang Liu\",\"doi\":\"10.1109/CSO.2012.173\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Using advantages of priori Bayesian method, a reliability verification test method based on Hybrid Bayesian Prior Distribution was brought forward. The prior distribution of unknown parameters can be obtained by using conjugate prior distribution method. Prior moment method and Maximum entropy method were used respectively to calculate two different groups of parameters, and then two different prior distributions can be obtained. Then confidence factors of the two prior distributions were determined by using the second category maximum likelihood method, and the final distribution can be got by integrating there two group of parameters according to their weight. Instance proved that the prior distribution obtained by this method is more accurate, and can fit better with the real distribution.\",\"PeriodicalId\":170543,\"journal\":{\"name\":\"2012 Fifth International Joint Conference on Computational Sciences and Optimization\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Fifth International Joint Conference on Computational Sciences and Optimization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSO.2012.173\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Fifth International Joint Conference on Computational Sciences and Optimization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSO.2012.173","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Reliability Verification Test Model Based on Hybrid Bayesian Prior Distribution
Using advantages of priori Bayesian method, a reliability verification test method based on Hybrid Bayesian Prior Distribution was brought forward. The prior distribution of unknown parameters can be obtained by using conjugate prior distribution method. Prior moment method and Maximum entropy method were used respectively to calculate two different groups of parameters, and then two different prior distributions can be obtained. Then confidence factors of the two prior distributions were determined by using the second category maximum likelihood method, and the final distribution can be got by integrating there two group of parameters according to their weight. Instance proved that the prior distribution obtained by this method is more accurate, and can fit better with the real distribution.