Thomas M. Cover斯坦福大學電氣工程系、統(tǒng)計系教授。曾任IEEE信息論學會主席，現(xiàn)任數(shù)理統(tǒng)計研究所研究員、IEEE高級會員。1972年以論文“Broadcast Channels”榮獲信息論優(yōu)秀論文獎，1990年被選為“Shannon Lecturer”，這是信息論領(lǐng)域的最高榮譽。最近20年，他致力于信息論和統(tǒng)計學之間的關(guān)系。
圖書目錄
List of Figures 1 Introduction and Preview 1.1Preview of the book
2 Entropy,Relative Entropy and Mutual Information 2.1 Entropy 2.2 Joint entropy and conditional entropy 2.3 Relation entropy and mutual information 2.4 Relationship between entropy ad mutual information 2.5 Chain rules for entropy,relative entropy and mutual information 2.6 Jensen's inequality and its consequences 2.7 The log sum inequality and ist applications 2.8 Data processing inequality 2.9 The second law of thermodymamics 2.10 Sufficient statistics 2.11 Fano;s inequality Summary of Chapter Problems for Chatpter Historical notes
3 The Asymptotic Equipartition Property 3.1 The AEP 3.2 Consequences of the AEP:data compression 3.3 Hign prbability sets and the typical set Summary of Chapter 3 Problems for Chapter 3 Historical notes
4 Entropy Rates of a Stochastic Process 4.1 Markov chains 4.2 Entropy rate 4.3 Examply:Entropy rateof a random walk on a weighted graph 4.4 Hidden Markov models Summary of Chapter 4 Problems for Chapter 4 Historical notes
5 Data Compression 5.1 Examples of codes 5.2 Kraft inequality 5.3 Optimal codes 5.4 Bounds on the optimal codelength 5.5 Kraft inequality for uniquely decodable codes 5.6 Huffman codes 5.7 Some comments on Huffman codes 5.8 Optimality of Huffman codes 5.9 Shannon-Fano-Elias coding 5.10 Arithmetic coding 5.11 Competitive optimality of the Shannon code 5.12 Generation of discrete distributions from far coins Summary of Chapter 5 Problems for Chapter 5 Historical notes
6 Gambling and Data Compression 6.1 The horse race 6.2 Gambling and side information 6.3 Dependent horse races and entropy rate 6.4 The entropy of English 6.5 Data compression and gambling 6.6 Gambling estimate of the entropy of English Summary of Chapter 6 Problems for Chapter 6 Historical notes
7 Komogorov Complexity 7.1 Models of cmputation 7.2 Kolmogorov complexity:definitions and examples 7.3 Kolmogorov complexity and entropy 7.4 Kolmogorov complexity of integers 7.5 Algorithmically random and incompressible sequences 7.6 Unversal probability 7.7 The halting progblem and the non-computability of Kolmogorov complexity 7.8 Ω/164 7.9 Universal gambling 7.10 Occam's razor 7.11 Kolmogorov complexity and universal probability 7.12 The Dolmogorov sufficeient statistic Srmmary of Chapter Problems of Chapter 7 Historical notes
8 Channel Capacity 8.1 Examples of channel capacity 8.2 Symetric channels 8.3 Properties of channel capacity 8.4 Preview of the channel coding theorem 8.5 Definitions 8.6 Jointly typical sequences 8.7 The channle coding theorem 8.8 Zero-error codes 8.9 Fano'inequality and the converse tothe coding theorem 8.10 Equality in thd converse to the channel coding theorem 8.11 Hamming codes 8.12 Feedback capacity 8.13 The joint source channel coding theorem Summary of Chapter 8 Problems for Chapter 8 Historical notes
9 Differential Entropy 9.1 Definitions 9.2 The AEP for continuous random variables 9.3 Relation of differential entropy to discrete entropy 9.4 Joint and conditional differential entropy 9.5 Relative entroopy and mutual information 9.6 Propertise of differential entropy,relative entropy and mutual information 9.7 Differential entropy bound on discrete entropy Summary of Chapter 9 Problems for Chapter 9 Historical notes
10 The Gaussian Channel 10.1 The Gaussian channel:definitions 10.2 Converse to the coding theorem for Gaussian channels 10.3 Band-limited channels 10.4 Parallel Gaussian channels 10.5 Channels with colored Gaussian noise 10.6 Gaussian channels with feedback Summary of Chapter 10 Problems for Chapter 10 Historcal notes
11 Maximum Entropy and Spectral Estimation 11.1 Maximum entropy distributions 11.2 Examples 11.3 An anomalous maximum entropy problem 11.4 Spectrum estimation 11.5 Entropy rates of a Gaussian process 11.6 Burg's maximum entropy theorem Summary of Chapter 11 Problem for Chapter 11 Historical notes
12 Information Theory and Statistics 12.1 The method of types 12.2 The law of large numbers 12.3 Unuversal source coding 12.4 Large deviation theory 12.5 Examples of Sanov's theorem 12.6 The conditional limit theorem 12.7 Hypothesis testing 12.8 Stein' lemma 12.9 Chernoff bound 12.10 Lempel-Ziv coding 12.11 Fisher information and the Cramer-Rao inequality Summary of Chapter 12 Problems for Chapter 12 Historical notes
13 Rate Distortion Theory 13.1 Quantization 13.2 Definitions 13.3 Calculation of the rate distortion function 13.4 Converse to the rate distortion theorem 13.5 Achievability of the rate distortion function 13.6 Strongly typical sequences and rate distortion 13.7 Characterization of the rate distortion function 13.8 Computatio of chammel capacity and the rate distortion function Summary of Chapter 13 Probems for Chapter 13 Historical notes
14 Network Information Theory 14.1 Gaussian multiple user channels 14.2 Jointly typical sequences 14.3 The multiple access channel 14.4 Encoding of crrelated sources 14.5 Duality between Slepian-Wolf encoding and multiple access channels 14.6 The broadcast channel 14.7 The relay channel 14.8 Source coding with side information 14.9 Rate distortion with side information 14.10 General multiterminal networks Summary of Chapter 14 Problems for Chapter 14 Historical notes
15 Information Theory and the Stock Market 15.1 The stock market:sone definitions 15.2 Kuhn-Tucker characterizxation of the log-optimal potrfolio 15.3 Asymptotic optimality of the log-optimal porfolio 15.4 Side information and the doubling rate 15.5 Investment in stationary markets 15.6 Competitive optimality of the log-optimal protfolio 15.7 The Shannon-McMillan-Breiman theorem Summary of Charpter 15 Problems for Charpter 15 Historical notes
16 Inequalities in Theory 16.1 Basic inequalities of information theory 16.2 Differential etropy 16.3 Bounds on entropy and relative entropy 16.4 Inequalities for types 16.5 Entropy rates of subsets 16.6 Entropy and Fisher information 16.7 The entropy power inequality and the BrunnMinkowski inequality 16.8 Inequalites for determinants 16.9 Inequalites for ratios of determinants Overall Summary Problems for Chapter Historical notes
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