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Information-spectrum methods in information theory

Information-spectrum methods in information theory

1星价 ¥110.9 (8.6折)
2星价¥110.9 定价¥129.0
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  • ISBN:9787519296896
  • 装帧:一般胶版纸
  • 册数:暂无
  • 重量:暂无
  • 开本:24cm
  • 页数:17,538页
  • 出版时间:2023-01-01
  • 条形码:9787519296896 ; 978-7-5192-9689-6

内容简介

《信息论的信息谱方法》由2010年获得被称为“信息领域的诺贝尔奖”的信息论领域*高荣誉——香农奖的韩太舜(Te Sun Han)所著,他任职于日本电气通信大学,发表了多篇论文与多部作品。 本书聚焦于任意非平稳的非遍历信源和信道,很好地补充了现有文献在信息论和编码理论方面内容的不足。本书有三大特点:一是别具特色的讲述方式——虽然内容主题比较常见,但作者在阐述各种概念定理时采用了非传统的方式,让人眼前一亮;二是作者广阔的知识面和独特的思维为许多问题提供了新的见解,富有原创性;三是本书的内容丰富详实,还包含了相当多的历史评论和大量的参考书目,为读者进一步阅读拓展知识面提供了参考书目。

目录

1 Source Coding 1.1 Source Coding: Fixed-Length Codes 1.2 Source Coding: Variable-Length Codes 1.3 Coding for General Sources: Fixed-Length Codes 1.4 Fixed-Length Coding for Mixed Sources 1.5 Strong Converse Theorem for Source Coding 1.6 ε-Source Coding 1.7 Coding for General Sources: Variable-Length Codes 1.8 Coding for General Source: Weak Variable-Length Codes 1.9 Source Coding and Large Deviation: Decoding Error Probability 1.10 Source Coding and Large Deviation: Probability of Correct Decoding 1.11 Reliability Functions of the General Source with Variable-Length Coding 1.12 Information Spectrum and Invariancy 2 Random Number Generation 2.1 Random Number Generation 2.2 Resolvability and Intrinsic Randomness 2.3 Strong Converse Theorem for Random Number Generation 2.4 δ-Random Number Generation 2.5 Variable-Length Intrinsic Randomness 2.6 Random Number Generation and Source Coding 3 Channel Coding 3.1 Channel Coding: Stationary Memoryless Channel 3.2 Coding for General Channel 3.3 Coding for Mixed Channels 3.4 ε-Channel Coding 3.5 Strong Converse Theorem on Channel Coding 3.6 Channel Capacity with Cost Constraint 3.7 Strong Converse Property of Channel with Cost Constraint 3.8 Joint Source-Channel Coding 3.9 Separation Theorems of the Traditional Type 4 Hypothesis Testing 4.1 Hypothesis Testing 4.2 ε-Hypothesis Testing 4.3 Strong Converse Theorem for Hypothesis Testing 4.4 Hypothesis Testing and Large Deviation Probability ofTesting Error 4.5 Hypothesis Testing and Large Deviation: Probability ofCorrect Testing 4.6 Generalized Hypothesis Testing 4.7 Hypothesis Testing and Source Coding 5 Rate-Distortion Theory 5.1 Coding Subject to Distortion Criterion 5.2 Rate-Distortion Theory for Stationary Memoryless Sources 5.3 General Rate-Distortion Theory 5.4 Rate-Distortion Function Rfm(DX) 5.5 Rate-Distortion Function Rfa(DX) 5.6 Rate-Distortion Function Rum(DX) 5.7 Rate-Distortion Function Rua(DX) 5.8 Rate-Distortion for Stationary Memoryless Sources Revisited 5.9 Rate-Distortion for Stationary Ergodic Sources 5.10 Rate-Distortion Function for Mixed Sources 6 Identification Code and Channel Resolvability
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作者简介

韩太舜(Te Sun Han),任职于日本电气通信大学,2010年获得信息论领域*高容易,被称为“信息领域的诺贝尔奖”的香农奖,发表了多篇论文与多部作品。

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