{"title":"树上马尔可夫链的共享信息","authors":"Sagnik Bhattacharya, P. Narayan","doi":"10.1109/ISIT50566.2022.9834365","DOIUrl":null,"url":null,"abstract":"Shared information is a measure of mutual dependence among m ≥ 2 jointly distributed discrete random variables. For a Markov chain on a tree with a given joint distribution, we give a new proof of an explicit characterization of shared information. When the joint distribution is not known, we exploit the special form of this characterization to provide a multiarmed bandit algorithm for estimating shared information, and analyze its error performance.","PeriodicalId":348168,"journal":{"name":"2022 IEEE International Symposium on Information Theory (ISIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Shared Information for a Markov Chain on a Tree\",\"authors\":\"Sagnik Bhattacharya, P. Narayan\",\"doi\":\"10.1109/ISIT50566.2022.9834365\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Shared information is a measure of mutual dependence among m ≥ 2 jointly distributed discrete random variables. For a Markov chain on a tree with a given joint distribution, we give a new proof of an explicit characterization of shared information. When the joint distribution is not known, we exploit the special form of this characterization to provide a multiarmed bandit algorithm for estimating shared information, and analyze its error performance.\",\"PeriodicalId\":348168,\"journal\":{\"name\":\"2022 IEEE International Symposium on Information Theory (ISIT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Symposium on Information Theory (ISIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISIT50566.2022.9834365\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Symposium on Information Theory (ISIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIT50566.2022.9834365","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Shared information is a measure of mutual dependence among m ≥ 2 jointly distributed discrete random variables. For a Markov chain on a tree with a given joint distribution, we give a new proof of an explicit characterization of shared information. When the joint distribution is not known, we exploit the special form of this characterization to provide a multiarmed bandit algorithm for estimating shared information, and analyze its error performance.
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