Tugkan Batu, S. Dasgupta, Ravi Kumar, R. Rubinfeld
{"title":"近似熵的复杂度","authors":"Tugkan Batu, S. Dasgupta, Ravi Kumar, R. Rubinfeld","doi":"10.1145/509907.510005","DOIUrl":null,"url":null,"abstract":"The Shannon entropy is a measure of the randomness of a distribution, and plays a central role in statistics, information theory, and data compression. Knowing the entropy of a random source can shed light on the compressibility of data produced by such a source. We consider the complexity of approximating the entropy under various different assumptions on the way the input is presented.","PeriodicalId":193513,"journal":{"name":"Proceedings 17th IEEE Annual Conference on Computational Complexity","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"181","resultStr":"{\"title\":\"The complexity of approximating the entropy\",\"authors\":\"Tugkan Batu, S. Dasgupta, Ravi Kumar, R. Rubinfeld\",\"doi\":\"10.1145/509907.510005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Shannon entropy is a measure of the randomness of a distribution, and plays a central role in statistics, information theory, and data compression. Knowing the entropy of a random source can shed light on the compressibility of data produced by such a source. We consider the complexity of approximating the entropy under various different assumptions on the way the input is presented.\",\"PeriodicalId\":193513,\"journal\":{\"name\":\"Proceedings 17th IEEE Annual Conference on Computational Complexity\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"181\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 17th IEEE Annual Conference on Computational Complexity\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/509907.510005\",\"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 17th IEEE Annual Conference on Computational Complexity","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/509907.510005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Shannon entropy is a measure of the randomness of a distribution, and plays a central role in statistics, information theory, and data compression. Knowing the entropy of a random source can shed light on the compressibility of data produced by such a source. We consider the complexity of approximating the entropy under various different assumptions on the way the input is presented.
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