{"title":"Information concentration for convex measures","authors":"Jiange Li, M. Fradelizi, M. Madiman","doi":"10.1109/ISIT.2016.7541475","DOIUrl":null,"url":null,"abstract":"Sharp exponential deviation estimates for the information content as well as a sharp bound on the varentropy are obtained for convex probability measures on Euclidean spaces. These provide, in a sense, a nonasymptotic equipartition property for convex measures even in the absence of stationarity-type assumptions.","PeriodicalId":198767,"journal":{"name":"2016 IEEE International Symposium on Information Theory (ISIT)","volume":"205 3","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Symposium on Information Theory (ISIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIT.2016.7541475","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Sharp exponential deviation estimates for the information content as well as a sharp bound on the varentropy are obtained for convex probability measures on Euclidean spaces. These provide, in a sense, a nonasymptotic equipartition property for convex measures even in the absence of stationarity-type assumptions.
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