Jeffreys先验得到渐近极大极小冗余

Proceedings of 1994 Workshop on Information Theory and Statistics Pub Date : 1994-10-27 DOI:10.1109/WITS.1994.513856

B. S. Clarke, A. Barron

{"title":"Jeffreys先验得到渐近极大极小冗余","authors":"B. S. Clarke, A. Barron","doi":"10.1109/WITS.1994.513856","DOIUrl":null,"url":null,"abstract":"We determine the asymptotic minimax redundancy of universal data compression in a parametric setting and show that it corresponds to the use of Jeffreys prior. Statistically, this formulation of the coding problem can be interpreted in a prior selection context and in an estimation context.","PeriodicalId":423518,"journal":{"name":"Proceedings of 1994 Workshop on Information Theory and Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Jeffreys' prior yields the asymptotic minimax redundancy\",\"authors\":\"B. S. Clarke, A. Barron\",\"doi\":\"10.1109/WITS.1994.513856\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We determine the asymptotic minimax redundancy of universal data compression in a parametric setting and show that it corresponds to the use of Jeffreys prior. Statistically, this formulation of the coding problem can be interpreted in a prior selection context and in an estimation context.\",\"PeriodicalId\":423518,\"journal\":{\"name\":\"Proceedings of 1994 Workshop on Information Theory and Statistics\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-10-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1994 Workshop on Information Theory and Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WITS.1994.513856\",\"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 of 1994 Workshop on Information Theory and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WITS.1994.513856","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

我们确定了在参数设置下通用数据压缩的渐近极大极小冗余，并证明了它对应于杰弗里斯先验的使用。从统计学上讲，这个编码问题的公式可以在先验选择上下文和估计上下文中解释。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Jeffreys' prior yields the asymptotic minimax redundancy

We determine the asymptotic minimax redundancy of universal data compression in a parametric setting and show that it corresponds to the use of Jeffreys prior. Statistically, this formulation of the coding problem can be interpreted in a prior selection context and in an estimation context.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of 1994 Workshop on Information Theory and Statistics

自引率

0.00%

发文量