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Elements of Information Theory (Record no. 2428)

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000 -LEADER
fixed length control field	10070nam a22001937a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field	20240930104436.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	240930b \|\|\|\|\|\|\|\| \|\|\|\| 00\| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number	9780471241959
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number	003.54
Item number	COV
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name	Cover Thomas M
245 ## - TITLE STATEMENT
Title	Elements of Information Theory
Statement of responsibility, etc.	Cover Thomas M Thomas Joy A
250 ## - EDITION STATEMENT
Edition statement	2
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc.	New Jersey
Name of publisher, distributor, etc.	WILEY Hoboken,
Date of publication, distribution, etc.	2006
300 ## - PHYSICAL DESCRIPTION
Page number	748p
505 ## - FORMATTED CONTENTS NOTE
Title	1 Introduction and Preview 1<br/><br/>1.1 Preview of the Book 5<br/><br/>2 Entropy, Relative Entropy, and Mutual Information 13<br/><br/>2.1 Entropy 13<br/><br/>2.2 Joint Entropy and Conditional Entropy 16<br/><br/>2.3 Relative Entropy and Mutual Information 19<br/><br/>2.4 Relationship Between Entropy and Mutual Information 20<br/><br/>2.5 Chain Rules for Entropy, Relative Entropy, and Mutual Information 22<br/><br/>2.6 Jensen’s Inequality and Its Consequences 25<br/><br/>2.7 Log Sum Inequality and Its Applications 30<br/><br/>2.8 Data-Processing Inequality 34<br/><br/>2.9 Sufficient Statistics 35<br/><br/>2.10 Fano’s Inequality 37<br/><br/>Summary 41<br/><br/>Problems 43<br/><br/>Historical Notes 54<br/><br/>3 Asymptotic Equipartition Property 57<br/><br/>3.1 Asymptotic Equipartition Property Theorem 58<br/><br/>3.2 Consequences of the AEP: Data Compression 60<br/><br/>3.3 High-Probability Sets and the Typical Set 62<br/><br/>Summary 64<br/><br/>Problems 64<br/><br/>Historical Notes 69<br/><br/>4 Entropy Rates of a Stochastic Process 71<br/><br/>4.1 Markov Chains 71<br/><br/>4.2 Entropy Rate 74<br/><br/>4.3 Example: Entropy Rate of a Random Walk on a Weighted Graph 78<br/><br/>4.4 Second Law of Thermodynamics 81<br/><br/>4.5 Functions of Markov Chains 84<br/><br/>Summary 87<br/><br/>Problems 88<br/><br/>Historical Notes 100<br/><br/>5 Data Compression 103<br/><br/>5.1 Examples of Codes 103<br/><br/>5.2 Kraft Inequality 107<br/><br/>5.3 Optimal Codes 110<br/><br/>5.4 Bounds on the Optimal Code Length 112<br/><br/>5.5 Kraft Inequality for Uniquely Decodable Codes 115<br/><br/>5.6 Huffman Codes 118<br/><br/>5.7 Some Comments on Huffman Codes 120<br/><br/>5.8 Optimality of Huffman Codes 123<br/><br/>5.9 Shannon–Fano–Elias Coding 127<br/><br/>5.10 Competitive Optimality of the Shannon Code 130<br/><br/>5.11 Generation of Discrete Distributions from Fair Coins 134<br/><br/>Summary 141<br/><br/>Problems 142<br/><br/>Historical Notes 157<br/><br/>6 Gambling and Data Compression 159<br/><br/>6.1 The Horse Race 159<br/><br/>6.2 Gambling and Side Information 164<br/><br/>6.3 Dependent Horse Races and Entropy Rate 166<br/><br/>6.4 The Entropy of English 168<br/><br/>6.5 Data Compression and Gambling 171<br/><br/>6.6 Gambling Estimate of the Entropy of English 173<br/><br/>Summary 175<br/><br/>Problems 176<br/><br/>Historical Notes 182<br/><br/>7 Channel Capacity 183<br/><br/>7.1 Examples of Channel Capacity 184<br/><br/>7.1.1 Noiseless Binary Channel 184<br/><br/>7.1.2 Noisy Channel with Nonoverlapping Outputs 185<br/><br/>7.1.3 Noisy Typewriter 186<br/><br/>7.1.4 Binary Symmetric Channel 187<br/><br/>7.1.5 Binary Erasure Channel 188<br/><br/>7.2 Symmetric Channels 189<br/><br/>7.3 Properties of Channel Capacity 191<br/><br/>7.4 Preview of the Channel Coding Theorem 191<br/><br/>7.5 Definitions 192<br/><br/>7.6 Jointly Typical Sequences 195<br/><br/>7.7 Channel Coding Theorem 199<br/><br/>7.8 Zero-Error Codes 205<br/><br/>7.9 Fano’s Inequality and the Converse to the Coding Theorem 206<br/><br/>7.10 Equality in the Converse to the Channel Coding Theorem 208<br/><br/>7.11 Hamming Codes 210<br/><br/>7.12 Feedback Capacity 216<br/><br/>7.13 Source–Channel Separation Theorem 218<br/><br/>Summary 222<br/><br/>Problems 223<br/><br/>Historical Notes 240<br/><br/>8 Differential Entropy 243<br/><br/>8.1 Definitions 243<br/><br/>8.2 AEP for Continuous Random Variables 245<br/><br/>8.3 Relation of Differential Entropy to Discrete Entropy 247<br/><br/>8.4 Joint and Conditional Differential Entropy 249<br/><br/>8.5 Relative Entropy and Mutual Information 250<br/><br/>8.6 Properties of Differential Entropy, Relative Entropy, and Mutual Information 252<br/><br/>Summary 256<br/><br/>Problems 256<br/><br/>Historical Notes 259<br/><br/>9 Gaussian Channel 261<br/><br/>9.1 Gaussian Channel: Definitions 263<br/><br/>9.2 Converse to the Coding Theorem for Gaussian Channels 268<br/><br/>9.3 Bandlimited Channels 270<br/><br/>9.4 Parallel Gaussian Channels 274<br/><br/>9.5 Channels with Colored Gaussian Noise 277<br/><br/>9.6 Gaussian Channels with Feedback 280<br/><br/>Summary 289<br/><br/>Problems 290<br/><br/>Historical Notes 299<br/><br/>10 Rate Distortion Theory 301<br/><br/>10.1 Quantization 301<br/><br/>10.2 Definitions 303<br/><br/>10.3 Calculation of the Rate Distortion Function 307<br/><br/>10.3.1 Binary Source 307<br/><br/>10.3.2 Gaussian Source 310<br/><br/>10.3.3 Simultaneous Description of Independent Gaussian Random Variables 312<br/><br/>10.4 Converse to the Rate Distortion Theorem 315<br/><br/>10.5 Achievability of the Rate Distortion Function 318<br/><br/>10.6 Strongly Typical Sequences and Rate Distortion 325<br/><br/>10.7 Characterization of the Rate Distortion Function 329<br/><br/>10.8 Computation of Channel Capacity and the Rate Distortion Function 332<br/><br/>Summary 335<br/><br/>Problems 336<br/><br/>Historical Notes 345<br/><br/>11 Information Theory and Statistics 347<br/><br/>11.1 Method of Types 347<br/><br/>11.2 Law of Large Numbers 355<br/><br/>11.3 Universal Source Coding 357<br/><br/>11.4 Large Deviation Theory 360<br/><br/>11.5 Examples of Sanov’s Theorem 364<br/><br/>11.6 Conditional Limit Theorem 366<br/><br/>11.7 Hypothesis Testing 375<br/><br/>11.8 Chernoff–Stein Lemma 380<br/><br/>11.9 Chernoff Information 384<br/><br/>11.10 Fisher Information and the Cramér–Rao Inequality 392<br/><br/>Summary 397<br/><br/>Problems 399<br/><br/>Historical Notes 408<br/><br/>12 Maximum Entropy 409<br/><br/>12.1 Maximum Entropy Distributions 409<br/><br/>12.2 Examples 411<br/><br/>12.3 Anomalous Maximum Entropy Problem 413<br/><br/>12.4 Spectrum Estimation 415<br/><br/>12.5 Entropy Rates of a Gaussian Process 416<br/><br/>12.6 Burg’s Maximum Entropy Theorem 417<br/><br/>Summary 420<br/><br/>Problems 421<br/><br/>Historical Notes 425<br/><br/>13 Universal Source Coding 427<br/><br/>13.1 Universal Codes and Channel Capacity 428<br/><br/>13.2 Universal Coding for Binary Sequences 433<br/><br/>13.3 Arithmetic Coding 436<br/><br/>13.4 Lempel–Ziv Coding 440<br/><br/>13.4.1 Sliding Window Lempel–Ziv Algorithm 441<br/><br/>13.4.2 Tree-Structured Lempel–Ziv Algorithms 442<br/><br/>13.5 Optimality of Lempel–Ziv Algorithms 443<br/><br/>13.5.1 Sliding Window Lempel–Ziv Algorithms 443<br/><br/>13.5.2 Optimality of Tree-Structured Lempel–Ziv Compression 448<br/><br/>Summary 456<br/><br/>Problems 457<br/><br/>Historical Notes 461<br/><br/>14 Kolmogorov Complexity 463<br/><br/>14.1 Models of Computation 464<br/><br/>14.2 Kolmogorov Complexity: Definitions and Examples 466<br/><br/>14.3 Kolmogorov Complexity and Entropy 473<br/><br/>14.4 Kolmogorov Complexity of Integers 475<br/><br/>14.5 Algorithmically Random and Incompressible Sequences 476<br/><br/>14.6 Universal Probability 480<br/><br/>14.7 Kolmogorov complexity 482<br/><br/>14.8 Ω 484<br/><br/>14.9 Universal Gambling 487<br/><br/>14.10 Occam’s Razor 488<br/><br/>14.11 Kolmogorov Complexity and Universal Probability 490<br/><br/>14.12 Kolmogorov Sufficient Statistic 496<br/><br/>14.13 Minimum Description Length Principle 500<br/><br/>Summary 501<br/><br/>Problems 503<br/><br/>Historical Notes 507<br/><br/>15 Network Information Theory 509<br/><br/>15.1 Gaussian Multiple-User Channels 513<br/><br/>15.1.1 Single-User Gaussian Channel 513<br/><br/>15.1.2 Gaussian Multiple-Access Channel with m Users 514<br/><br/>15.1.3 Gaussian Broadcast Channel 515<br/><br/>15.1.4 Gaussian Relay Channel 516<br/><br/>15.1.5 Gaussian Interference Channel 518<br/><br/>15.1.6 Gaussian Two-Way Channel 519<br/><br/>15.2 Jointly Typical Sequences 520<br/><br/>15.3 Multiple-Access Channel 524<br/><br/>15.3.1 Achievability of the Capacity Region for the Multiple-Access Channel 530<br/><br/>15.3.2 Comments on the Capacity Region for the Multiple-Access Channel 532<br/><br/>15.3.3 Convexity of the Capacity Region of the Multiple-Access Channel 534<br/><br/>15.3.4 Converse for the Multiple-Access Channel 538<br/><br/>15.3.5 m-User Multiple-Access Channels 543<br/><br/>15.3.6 Gaussian Multiple-Access Channels 544<br/><br/>15.4 Encoding of Correlated Sources 549<br/><br/>15.4.1 Achievability of the Slepian–Wolf Theorem 551<br/><br/>15.4.2 Converse for the Slepian–Wolf Theorem 555<br/><br/>15.4.3 Slepian–Wolf Theorem for Many Sources 556<br/><br/>15.4.4 Interpretation of Slepian–Wolf Coding 557<br/><br/>15.5 Duality Between Slepian–Wolf Encoding and Multiple-Access Channels 558<br/><br/>15.6 Broadcast Channel 560<br/><br/>15.6.1 Definitions for a Broadcast Channel 563<br/><br/>15.6.2 Degraded Broadcast Channels 564<br/><br/>15.6.3 Capacity Region for the Degraded Broadcast Channel 565<br/><br/>15.7 Relay Channel 571<br/><br/>15.8 Source Coding with Side Information 575<br/><br/>15.9 Rate Distortion with Side Information 580<br/><br/>15.10 General Multiterminal Networks 587<br/><br/>Summary 594<br/><br/>Problems 596<br/><br/>Historical Notes 609<br/><br/>16 Information Theory and Portfolio Theory 613<br/><br/>16.1 The Stock Market: Some Definitions 613<br/><br/>16.2 Kuhn–Tucker Characterization of the Log-Optimal Portfolio 617<br/><br/>16.3 Asymptotic Optimality of the Log-Optimal Portfolio 619<br/><br/>16.4 Side Information and the Growth Rate 621<br/><br/>16.5 Investment in Stationary Markets 623<br/><br/>16.6 Competitive Optimality of the Log-Optimal Portfolio 627<br/><br/>16.7 Universal Portfolios 629<br/><br/>16.7.1 Finite-Horizon Universal Portfolios 631<br/><br/>16.7.2 Horizon-Free Universal Portfolios 638<br/><br/>16.8 Shannon–McMillan–Breiman Theorem (General AEP) 644<br/><br/>Summary 650<br/><br/>Problems 652<br/><br/>Historical Notes 655<br/><br/>17 Inequalities in Information Theory 657<br/><br/>17.1 Basic Inequalities of Information Theory 657<br/><br/>17.2 Differential Entropy 660<br/><br/>17.3 Bounds on Entropy and Relative Entropy 663<br/><br/>17.4 Inequalities for Types 665<br/><br/>17.5 Combinatorial Bounds on Entropy 666<br/><br/>17.6 Entropy Rates of Subsets 667<br/><br/>17.7 Entropy and Fisher Information 671<br/><br/>17.8 Entropy Power Inequality and Brunn–Minkowski Inequality 674<br/><br/>17.9 Inequalities for Determinants 679<br/><br/>17.10 Inequalities for Ratios of Determinants 683
520 ## - SUMMARY, ETC.
Summary, etc.	The Second Edition of this fundamental textbook maintains the book's tradition of clear, thought-provoking instruction. Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory.<br/><br/>All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide readers with a solid understanding of the underlying theory and applications. Problem sets and a telegraphic summary at the end of each chapter further assist readers.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element	Information Theory Data Compression Entropy, Relative Entropy, and Mutual Information
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme	Dewey Decimal Classification
Koha item type	Books
952 ## - LOCATION AND ITEM INFORMATION (KOHA)
--	7984

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Withdrawn status	Lost status	Source of classification or shelving scheme	Damaged status	Not for loan	Collection code	Home library	Current library	Shelving location	Date acquired	Source of acquisition	Cost, normal purchase price	Inventory number	Total Checkouts	Full call number	Barcode	Date last seen	Cost, replacement price	Price effective from	Currency	Koha item type
		Dewey Decimal Classification			Non-fiction	IIITDM Kurnool	IIITDM Kurnool	ELECTRONICS COMMUNICATION ENGINEERING	30.09.2024	New India Book Agency	132.95	5395 dt 31-8-2024		003.54 COV	0006966	30.09.2024	132.95	30.09.2024	USD	Books