{"title":"多状态系统的统计推断:威布尔情况","authors":"A. Makrides, A. Karagrigoriou","doi":"10.1109/ARES.2013.99","DOIUrl":null,"url":null,"abstract":"Markov processes are widely used for reliability analysis because the number of failures in arbitrary time intervals in many practical cases can be described as a Poisson process and the time up to the failure and repair time are often exponentially distributed. In this work we focus on the estimation of both the intensity rates and transition probabilities via output performance observations using as an alternative distribution, the well known Weibull distribution.","PeriodicalId":302747,"journal":{"name":"2013 International Conference on Availability, Reliability and Security","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Statistical Inference for Multi-state Systems: The Weibull Case\",\"authors\":\"A. Makrides, A. Karagrigoriou\",\"doi\":\"10.1109/ARES.2013.99\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Markov processes are widely used for reliability analysis because the number of failures in arbitrary time intervals in many practical cases can be described as a Poisson process and the time up to the failure and repair time are often exponentially distributed. In this work we focus on the estimation of both the intensity rates and transition probabilities via output performance observations using as an alternative distribution, the well known Weibull distribution.\",\"PeriodicalId\":302747,\"journal\":{\"name\":\"2013 International Conference on Availability, Reliability and Security\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on Availability, Reliability and Security\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ARES.2013.99\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Availability, Reliability and Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ARES.2013.99","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Statistical Inference for Multi-state Systems: The Weibull Case
Markov processes are widely used for reliability analysis because the number of failures in arbitrary time intervals in many practical cases can be described as a Poisson process and the time up to the failure and repair time are often exponentially distributed. In this work we focus on the estimation of both the intensity rates and transition probabilities via output performance observations using as an alternative distribution, the well known Weibull distribution.