{"title":"概率系统的随机性:分析方法-个案研究","authors":"Anwitaman Datta, M. Hasler, K. Aberer","doi":"10.1109/COLCOM.2005.1651267","DOIUrl":null,"url":null,"abstract":"We do a case study of two different analysis techniques for studying the stochastic behavior of a randomized system/algorithms: (i) The first approach can be broadly termed as a mean value analysis (MVA), where the evolution of the mean state is studied assuming that the system always actually resides in the mean state; (ii) The second approach looks at the probability distribution function of the system states at any time instance, thus studying the evolution of the (probability mass) distribution function (EoDF)","PeriodicalId":365186,"journal":{"name":"2005 International Conference on Collaborative Computing: Networking, Applications and Worksharing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Stochasticity of probabilistic systems: analysis methodologies case-study\",\"authors\":\"Anwitaman Datta, M. Hasler, K. Aberer\",\"doi\":\"10.1109/COLCOM.2005.1651267\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We do a case study of two different analysis techniques for studying the stochastic behavior of a randomized system/algorithms: (i) The first approach can be broadly termed as a mean value analysis (MVA), where the evolution of the mean state is studied assuming that the system always actually resides in the mean state; (ii) The second approach looks at the probability distribution function of the system states at any time instance, thus studying the evolution of the (probability mass) distribution function (EoDF)\",\"PeriodicalId\":365186,\"journal\":{\"name\":\"2005 International Conference on Collaborative Computing: Networking, Applications and Worksharing\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2005 International Conference on Collaborative Computing: Networking, Applications and Worksharing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COLCOM.2005.1651267\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 International Conference on Collaborative Computing: Networking, Applications and Worksharing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COLCOM.2005.1651267","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Stochasticity of probabilistic systems: analysis methodologies case-study
We do a case study of two different analysis techniques for studying the stochastic behavior of a randomized system/algorithms: (i) The first approach can be broadly termed as a mean value analysis (MVA), where the evolution of the mean state is studied assuming that the system always actually resides in the mean state; (ii) The second approach looks at the probability distribution function of the system states at any time instance, thus studying the evolution of the (probability mass) distribution function (EoDF)
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