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Information-spectrum methods in information theory 版權信息
- ISBN：9787519296896
- 條形碼：9787519296896 ; 978-7-5192-9689-6
- 裝幀：一般膠版紙
- 冊數：暫無
- 重量：暫無
- 所屬分類：>>
Information-spectrum methods in information theory 內容簡介
《信息論的信息譜方法》由2010年獲得被稱為“信息領域的諾貝爾獎”的信息論領域*高榮譽——香農獎的韓太舜(Te Sun Han)所著，他任職于日本電氣通信大學，發表了多篇論文與多部作品。 本書聚焦于任意非平穩的非遍歷信源和信道，很好地補充了現有文獻在信息論和編碼理論方面內容的不足。本書有三大特點：一是別具特色的講述方式——雖然內容主題比較常見，但作者在闡述各種概念定理時采用了非傳統的方式，讓人眼前一亮；二是作者廣闊的知識面和獨特的思維為許多問題提供了新的見解，富有原創性；三是本書的內容豐富詳實，還包含了相當多的歷史評論和大量的參考書目，為讀者進一步閱讀拓展知識面提供了參考書目。
Information-spectrum methods in information theory 目錄
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
展開全部
Information-spectrum methods in information theory 作者簡介
韓太舜（Te Sun Han），任職于日本電氣通信大學，2010年獲得信息論領域最高容易，被稱為“信息領域的諾貝爾獎”的香農獎，發表了多篇論文與多部作品。
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