{"title":"收敛模式","authors":"M. Proschan, P. Shaw","doi":"10.1201/9781315370576-6","DOIUrl":null,"url":null,"abstract":"the sequence after N is always within ε of the supposed limit a. In contrast, the notion of convergence becomes somewhat more subtle when discussing convergence of functions. In this note we briefly describe a few modes of convergence and explain their relationship. Since the subject quickly becomes very technical, we will state many of the fundamental results without proof. Throughout this discussion, fix a probability space Ω and a sequence of random variables (Xn)n∈N. Also, let X be another random variable.","PeriodicalId":424052,"journal":{"name":"Essentials of Probability Theory for Statisticians","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modes of Convergence\",\"authors\":\"M. Proschan, P. Shaw\",\"doi\":\"10.1201/9781315370576-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"the sequence after N is always within ε of the supposed limit a. In contrast, the notion of convergence becomes somewhat more subtle when discussing convergence of functions. In this note we briefly describe a few modes of convergence and explain their relationship. Since the subject quickly becomes very technical, we will state many of the fundamental results without proof. Throughout this discussion, fix a probability space Ω and a sequence of random variables (Xn)n∈N. Also, let X be another random variable.\",\"PeriodicalId\":424052,\"journal\":{\"name\":\"Essentials of Probability Theory for Statisticians\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Essentials of Probability Theory for Statisticians\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1201/9781315370576-6\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Essentials of Probability Theory for Statisticians","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1201/9781315370576-6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
the sequence after N is always within ε of the supposed limit a. In contrast, the notion of convergence becomes somewhat more subtle when discussing convergence of functions. In this note we briefly describe a few modes of convergence and explain their relationship. Since the subject quickly becomes very technical, we will state many of the fundamental results without proof. Throughout this discussion, fix a probability space Ω and a sequence of random variables (Xn)n∈N. Also, let X be another random variable.