{"title":"比例因子:相位型近似中的一种新的自由度","authors":"A. Bobbio, A. Horváth, M. Telek","doi":"10.1109/DSN.2002.1029008","DOIUrl":null,"url":null,"abstract":"This paper introduces a unified approach to phase-type approximation in which the discrete and continuous phase-type models form a common model set. The models of this common set are assigned with a non-negative real parameter, the scale factor. The case when the scale factor is strictly positive results in discrete phase-type distributions and the scale factor represents the time elapsed in one step. If the scale factor is 0, the resulting class is the class of continuous phase-type distributions. Applying the above view, it is shown that there is no qualitative difference between the discrete and the continuous phase-type models. Based on this unified view of phase-type models one can choose the best phase-type approximation of a stochastic model by optimizing the scale factor.","PeriodicalId":93807,"journal":{"name":"Proceedings. International Conference on Dependable Systems and Networks","volume":"11 1","pages":"627-636"},"PeriodicalIF":0.0000,"publicationDate":"2002-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"42","resultStr":"{\"title\":\"The scale factor: a new degree of freedom in phase type approximation\",\"authors\":\"A. Bobbio, A. Horváth, M. Telek\",\"doi\":\"10.1109/DSN.2002.1029008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces a unified approach to phase-type approximation in which the discrete and continuous phase-type models form a common model set. The models of this common set are assigned with a non-negative real parameter, the scale factor. The case when the scale factor is strictly positive results in discrete phase-type distributions and the scale factor represents the time elapsed in one step. If the scale factor is 0, the resulting class is the class of continuous phase-type distributions. Applying the above view, it is shown that there is no qualitative difference between the discrete and the continuous phase-type models. Based on this unified view of phase-type models one can choose the best phase-type approximation of a stochastic model by optimizing the scale factor.\",\"PeriodicalId\":93807,\"journal\":{\"name\":\"Proceedings. International Conference on Dependable Systems and Networks\",\"volume\":\"11 1\",\"pages\":\"627-636\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"42\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. International Conference on Dependable Systems and Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DSN.2002.1029008\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Conference on Dependable Systems and Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSN.2002.1029008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The scale factor: a new degree of freedom in phase type approximation
This paper introduces a unified approach to phase-type approximation in which the discrete and continuous phase-type models form a common model set. The models of this common set are assigned with a non-negative real parameter, the scale factor. The case when the scale factor is strictly positive results in discrete phase-type distributions and the scale factor represents the time elapsed in one step. If the scale factor is 0, the resulting class is the class of continuous phase-type distributions. Applying the above view, it is shown that there is no qualitative difference between the discrete and the continuous phase-type models. Based on this unified view of phase-type models one can choose the best phase-type approximation of a stochastic model by optimizing the scale factor.