{"product_id":"elements-of-information-theory-hardback-9780471241959","title":"Elements of Information Theory (Hardback) 9780471241959","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eElements of Information Theory\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eThomas M. Cover (Author), Joy A. Thomas (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780471241959, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 8 September 2006\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e784 pages\u003cbr\u003e24.1 x 16.3 x 4.1 cm, 1.157 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"As expected, the quality of exposition continues to be a high point of the book. Clear explanations, nice graphical illustrations, and illuminating mathematical derivations make the book particularly useful as a textbook on information theory.\" (Journal of the American Statistical Association, March 2008)\u003cbr\u003e \u003cbr\u003e \"This book is recommended reading, both as a textbook and as a reference.\" (Computing Reviews.com, December 28, 2006)\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eThe latest edition of this classic is updated with new problem sets and material\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe \u003ci\u003eSecond Edition\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eAll 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. The historical notes that follow each chapter recap the main points.\u003c\/p\u003e \u003cp\u003eThe \u003ci\u003eSecond Edition\u003c\/i\u003e features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eChapters reorganized to improve teaching\u003c\/li\u003e \u003cli\u003e200 new problems\u003c\/li\u003e \u003cli\u003eNew material on source coding, portfolio theory, and feedback capacity\u003c\/li\u003e \u003cli\u003eUpdated references\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eNow current and enhanced, the Second Edition of \u003ci\u003eElements of Information Theory\u003c\/i\u003e remains the ideal textbook for upper-level undergraduate and graduate courses in electrical engineering, statistics, and telecommunications.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eContents v\u003c\/p\u003e \u003cp\u003ePreface to the Second Edition xv\u003c\/p\u003e \u003cp\u003ePreface to the First Edition xvii\u003c\/p\u003e \u003cp\u003eAcknowledgments for the Second Edition xxi\u003c\/p\u003e \u003cp\u003eAcknowledgments for the First Edition xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction and Preview 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Preview of the Book 5\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Entropy, Relative Entropy, and Mutual Information 13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Entropy 13\u003c\/p\u003e \u003cp\u003e2.2 Joint Entropy and Conditional Entropy 16\u003c\/p\u003e \u003cp\u003e2.3 Relative Entropy and Mutual Information 19\u003c\/p\u003e \u003cp\u003e2.4 Relationship Between Entropy and Mutual Information 20\u003c\/p\u003e \u003cp\u003e2.5 Chain Rules for Entropy, Relative Entropy, and Mutual Information 22\u003c\/p\u003e \u003cp\u003e2.6 Jensen’s Inequality and Its Consequences 25\u003c\/p\u003e \u003cp\u003e2.7 Log Sum Inequality and Its Applications 30\u003c\/p\u003e \u003cp\u003e2.8 Data-Processing Inequality 34\u003c\/p\u003e \u003cp\u003e2.9 Sufficient Statistics 35\u003c\/p\u003e \u003cp\u003e2.10 Fano’s Inequality 37\u003c\/p\u003e \u003cp\u003eSummary 41\u003c\/p\u003e \u003cp\u003eProblems 43\u003c\/p\u003e \u003cp\u003eHistorical Notes 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Asymptotic Equipartition Property 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Asymptotic Equipartition Property Theorem 58\u003c\/p\u003e \u003cp\u003e3.2 Consequences of the AEP: Data Compression 60\u003c\/p\u003e \u003cp\u003e3.3 High-Probability Sets and the Typical Set 62\u003c\/p\u003e \u003cp\u003eSummary 64\u003c\/p\u003e \u003cp\u003eProblems 64\u003c\/p\u003e \u003cp\u003eHistorical Notes 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Entropy Rates of a Stochastic Process 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Markov Chains 71\u003c\/p\u003e \u003cp\u003e4.2 Entropy Rate 74\u003c\/p\u003e \u003cp\u003e4.3 Example: Entropy Rate of a Random Walk on a Weighted Graph 78\u003c\/p\u003e \u003cp\u003e4.4 Second Law of Thermodynamics 81\u003c\/p\u003e \u003cp\u003e4.5 Functions of Markov Chains 84\u003c\/p\u003e \u003cp\u003eSummary 87\u003c\/p\u003e \u003cp\u003eProblems 88\u003c\/p\u003e \u003cp\u003eHistorical Notes 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Data Compression 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Examples of Codes 103\u003c\/p\u003e \u003cp\u003e5.2 Kraft Inequality 107\u003c\/p\u003e \u003cp\u003e5.3 Optimal Codes 110\u003c\/p\u003e \u003cp\u003e5.4 Bounds on the Optimal Code Length 112\u003c\/p\u003e \u003cp\u003e5.5 Kraft Inequality for Uniquely Decodable Codes 115\u003c\/p\u003e \u003cp\u003e5.6 Huffman Codes 118\u003c\/p\u003e \u003cp\u003e5.7 Some Comments on Huffman Codes 120\u003c\/p\u003e \u003cp\u003e5.8 Optimality of Huffman Codes 123\u003c\/p\u003e \u003cp\u003e5.9 Shannon–Fano–Elias Coding 127\u003c\/p\u003e \u003cp\u003e5.10 Competitive Optimality of the Shannon Code 130\u003c\/p\u003e \u003cp\u003e5.11 Generation of Discrete Distributions from Fair Coins 134\u003c\/p\u003e \u003cp\u003eSummary 141\u003c\/p\u003e \u003cp\u003eProblems 142\u003c\/p\u003e \u003cp\u003eHistorical Notes 157\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Gambling and Data Compression 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 The Horse Race 159\u003c\/p\u003e \u003cp\u003e6.2 Gambling and Side Information 164\u003c\/p\u003e \u003cp\u003e6.3 Dependent Horse Races and Entropy Rate 166\u003c\/p\u003e \u003cp\u003e6.4 The Entropy of English 168\u003c\/p\u003e \u003cp\u003e6.5 Data Compression and Gambling 171\u003c\/p\u003e \u003cp\u003e6.6 Gambling Estimate of the Entropy of English 173\u003c\/p\u003e \u003cp\u003eSummary 175\u003c\/p\u003e \u003cp\u003eProblems 176\u003c\/p\u003e \u003cp\u003eHistorical Notes 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Channel Capacity 183\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Examples of Channel Capacity 184\u003c\/p\u003e \u003cp\u003e7.1.1 Noiseless Binary Channel 184\u003c\/p\u003e \u003cp\u003e7.1.2 Noisy Channel with Nonoverlapping Outputs 185\u003c\/p\u003e \u003cp\u003e7.1.3 Noisy Typewriter 186\u003c\/p\u003e \u003cp\u003e7.1.4 Binary Symmetric Channel 187\u003c\/p\u003e \u003cp\u003e7.1.5 Binary Erasure Channel 188\u003c\/p\u003e \u003cp\u003e7.2 Symmetric Channels 189\u003c\/p\u003e \u003cp\u003e7.3 Properties of Channel Capacity 191\u003c\/p\u003e \u003cp\u003e7.4 Preview of the Channel Coding Theorem 191\u003c\/p\u003e \u003cp\u003e7.5 Definitions 192\u003c\/p\u003e \u003cp\u003e7.6 Jointly Typical Sequences 195\u003c\/p\u003e \u003cp\u003e7.7 Channel Coding Theorem 199\u003c\/p\u003e \u003cp\u003e7.8 Zero-Error Codes 205\u003c\/p\u003e \u003cp\u003e7.9 Fano’s Inequality and the Converse to the Coding Theorem 206\u003c\/p\u003e \u003cp\u003e7.10 Equality in the Converse to the Channel Coding Theorem 208\u003c\/p\u003e \u003cp\u003e7.11 Hamming Codes 210\u003c\/p\u003e \u003cp\u003e7.12 Feedback Capacity 216\u003c\/p\u003e \u003cp\u003e7.13 Source–Channel Separation Theorem 218\u003c\/p\u003e \u003cp\u003eSummary 222\u003c\/p\u003e \u003cp\u003eProblems 223\u003c\/p\u003e \u003cp\u003eHistorical Notes 240\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Differential Entropy 243\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Definitions 243\u003c\/p\u003e \u003cp\u003e8.2 AEP for Continuous Random Variables 245\u003c\/p\u003e \u003cp\u003e8.3 Relation of Differential Entropy to Discrete Entropy 247\u003c\/p\u003e \u003cp\u003e8.4 Joint and Conditional Differential Entropy 249\u003c\/p\u003e \u003cp\u003e8.5 Relative Entropy and Mutual Information 250\u003c\/p\u003e \u003cp\u003e8.6 Properties of Differential Entropy, Relative Entropy, and Mutual Information 252\u003c\/p\u003e \u003cp\u003eSummary 256\u003c\/p\u003e \u003cp\u003eProblems 256\u003c\/p\u003e \u003cp\u003eHistorical Notes 259\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Gaussian Channel 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Gaussian Channel: Definitions 263\u003c\/p\u003e \u003cp\u003e9.2 Converse to the Coding Theorem for Gaussian Channels 268\u003c\/p\u003e \u003cp\u003e9.3 Bandlimited Channels 270\u003c\/p\u003e \u003cp\u003e9.4 Parallel Gaussian Channels 274\u003c\/p\u003e \u003cp\u003e9.5 Channels with Colored Gaussian Noise 277\u003c\/p\u003e \u003cp\u003e9.6 Gaussian Channels with Feedback 280\u003c\/p\u003e \u003cp\u003eSummary 289\u003c\/p\u003e \u003cp\u003eProblems 290\u003c\/p\u003e \u003cp\u003eHistorical Notes 299\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Rate Distortion Theory 301\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Quantization 301\u003c\/p\u003e \u003cp\u003e10.2 Definitions 303\u003c\/p\u003e \u003cp\u003e10.3 Calculation of the Rate Distortion Function 307\u003c\/p\u003e \u003cp\u003e10.3.1 Binary Source 307\u003c\/p\u003e \u003cp\u003e10.3.2 Gaussian Source 310\u003c\/p\u003e \u003cp\u003e10.3.3 Simultaneous Description of Independent Gaussian Random Variables 312\u003c\/p\u003e \u003cp\u003e10.4 Converse to the Rate Distortion Theorem 315\u003c\/p\u003e \u003cp\u003e10.5 Achievability of the Rate Distortion Function 318\u003c\/p\u003e \u003cp\u003e10.6 Strongly Typical Sequences and Rate Distortion 325\u003c\/p\u003e \u003cp\u003e10.7 Characterization of the Rate Distortion Function 329\u003c\/p\u003e \u003cp\u003e10.8 Computation of Channel Capacity and the Rate Distortion Function 332\u003c\/p\u003e \u003cp\u003eSummary 335\u003c\/p\u003e \u003cp\u003eProblems 336\u003c\/p\u003e \u003cp\u003eHistorical Notes 345\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Information Theory and Statistics 347\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Method of Types 347\u003c\/p\u003e \u003cp\u003e11.2 Law of Large Numbers 355\u003c\/p\u003e \u003cp\u003e11.3 Universal Source Coding 357\u003c\/p\u003e \u003cp\u003e11.4 Large Deviation Theory 360\u003c\/p\u003e \u003cp\u003e11.5 Examples of Sanov’s Theorem 364\u003c\/p\u003e \u003cp\u003e11.6 Conditional Limit Theorem 366\u003c\/p\u003e \u003cp\u003e11.7 Hypothesis Testing 375\u003c\/p\u003e \u003cp\u003e11.8 Chernoff–Stein Lemma 380\u003c\/p\u003e \u003cp\u003e11.9 Chernoff Information 384\u003c\/p\u003e \u003cp\u003e11.10 Fisher Information and the Cramér–Rao Inequality 392\u003c\/p\u003e \u003cp\u003eSummary 397\u003c\/p\u003e \u003cp\u003eProblems 399\u003c\/p\u003e \u003cp\u003eHistorical Notes 408\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Maximum Entropy 409\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Maximum Entropy Distributions 409\u003c\/p\u003e \u003cp\u003e12.2 Examples 411\u003c\/p\u003e \u003cp\u003e12.3 Anomalous Maximum Entropy Problem 413\u003c\/p\u003e \u003cp\u003e12.4 Spectrum Estimation 415\u003c\/p\u003e \u003cp\u003e12.5 Entropy Rates of a Gaussian Process 416\u003c\/p\u003e \u003cp\u003e12.6 Burg’s Maximum Entropy Theorem 417\u003c\/p\u003e \u003cp\u003eSummary 420\u003c\/p\u003e \u003cp\u003eProblems 421\u003c\/p\u003e \u003cp\u003eHistorical Notes 425\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Universal Source Coding 427\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Universal Codes and Channel Capacity 428\u003c\/p\u003e \u003cp\u003e13.2 Universal Coding for Binary Sequences 433\u003c\/p\u003e \u003cp\u003e13.3 Arithmetic Coding 436\u003c\/p\u003e \u003cp\u003e13.4 Lempel–Ziv Coding 440\u003c\/p\u003e \u003cp\u003e13.4.1 Sliding Window Lempel–Ziv Algorithm 441\u003c\/p\u003e \u003cp\u003e13.4.2 Tree-Structured Lempel–Ziv Algorithms 442\u003c\/p\u003e \u003cp\u003e13.5 Optimality of Lempel–Ziv Algorithms 443\u003c\/p\u003e \u003cp\u003e13.5.1 Sliding Window Lempel–Ziv Algorithms 443\u003c\/p\u003e \u003cp\u003e13.5.2 Optimality of Tree-Structured Lempel–Ziv Compression 448\u003c\/p\u003e \u003cp\u003eSummary 456\u003c\/p\u003e \u003cp\u003eProblems 457\u003c\/p\u003e \u003cp\u003eHistorical Notes 461\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Kolmogorov Complexity 463\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Models of Computation 464\u003c\/p\u003e \u003cp\u003e14.2 Kolmogorov Complexity: Definitions and Examples 466\u003c\/p\u003e \u003cp\u003e14.3 Kolmogorov Complexity and Entropy 473\u003c\/p\u003e \u003cp\u003e14.4 Kolmogorov Complexity of Integers 475\u003c\/p\u003e \u003cp\u003e14.5 Algorithmically Random and Incompressible Sequences 476\u003c\/p\u003e \u003cp\u003e14.6 Universal Probability 480\u003c\/p\u003e \u003cp\u003e14.7 Kolmogorov complexity 482\u003c\/p\u003e \u003cp\u003e14.8 Ω 484\u003c\/p\u003e \u003cp\u003e14.9 Universal Gambling 487\u003c\/p\u003e \u003cp\u003e14.10 Occam’s Razor 488\u003c\/p\u003e \u003cp\u003e14.11 Kolmogorov Complexity and Universal Probability 490\u003c\/p\u003e \u003cp\u003e14.12 Kolmogorov Sufficient Statistic 496\u003c\/p\u003e \u003cp\u003e14.13 Minimum Description Length Principle 500\u003c\/p\u003e \u003cp\u003eSummary 501\u003c\/p\u003e \u003cp\u003eProblems 503\u003c\/p\u003e \u003cp\u003eHistorical Notes 507\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Network Information Theory 509\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Gaussian Multiple-User Channels 513\u003c\/p\u003e \u003cp\u003e15.1.1 Single-User Gaussian Channel 513\u003c\/p\u003e \u003cp\u003e15.1.2 Gaussian Multiple-Access Channel with \u003ci\u003em \u003c\/i\u003eUsers 514\u003c\/p\u003e \u003cp\u003e15.1.3 Gaussian Broadcast Channel 515\u003c\/p\u003e \u003cp\u003e15.1.4 Gaussian Relay Channel 516\u003c\/p\u003e \u003cp\u003e15.1.5 Gaussian Interference Channel 518\u003c\/p\u003e \u003cp\u003e15.1.6 Gaussian Two-Way Channel 519\u003c\/p\u003e \u003cp\u003e15.2 Jointly Typical Sequences 520\u003c\/p\u003e \u003cp\u003e15.3 Multiple-Access Channel 524\u003c\/p\u003e \u003cp\u003e15.3.1 Achievability of the Capacity Region for the Multiple-Access Channel 530\u003c\/p\u003e \u003cp\u003e15.3.2 Comments on the Capacity Region for the Multiple-Access Channel 532\u003c\/p\u003e \u003cp\u003e15.3.3 Convexity of the Capacity Region of the Multiple-Access Channel 534\u003c\/p\u003e \u003cp\u003e15.3.4 Converse for the Multiple-Access Channel 538\u003c\/p\u003e \u003cp\u003e15.3.5 \u003ci\u003em\u003c\/i\u003e-User Multiple-Access Channels 543\u003c\/p\u003e \u003cp\u003e15.3.6 Gaussian Multiple-Access Channels 544\u003c\/p\u003e \u003cp\u003e15.4 Encoding of Correlated Sources 549\u003c\/p\u003e \u003cp\u003e15.4.1 Achievability of the Slepian–Wolf Theorem 551\u003c\/p\u003e \u003cp\u003e15.4.2 Converse for the Slepian–Wolf Theorem 555\u003c\/p\u003e \u003cp\u003e15.4.3 Slepian–Wolf Theorem for Many Sources 556\u003c\/p\u003e \u003cp\u003e15.4.4 Interpretation of Slepian–Wolf Coding 557\u003c\/p\u003e \u003cp\u003e15.5 Duality Between Slepian–Wolf Encoding and Multiple-Access Channels 558\u003c\/p\u003e \u003cp\u003e15.6 Broadcast Channel 560\u003c\/p\u003e \u003cp\u003e15.6.1 Definitions for a Broadcast Channel 563\u003c\/p\u003e \u003cp\u003e15.6.2 Degraded Broadcast Channels 564\u003c\/p\u003e \u003cp\u003e15.6.3 Capacity Region for the Degraded Broadcast Channel 565\u003c\/p\u003e \u003cp\u003e15.7 Relay Channel 571\u003c\/p\u003e \u003cp\u003e15.8 Source Coding with Side Information 575\u003c\/p\u003e \u003cp\u003e15.9 Rate Distortion with Side Information 580\u003c\/p\u003e \u003cp\u003e15.10 General Multiterminal Networks 587\u003c\/p\u003e \u003cp\u003eSummary 594\u003c\/p\u003e \u003cp\u003eProblems 596\u003c\/p\u003e \u003cp\u003eHistorical Notes 609\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Information Theory and Portfolio Theory 613\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 The Stock Market: Some Definitions 613\u003c\/p\u003e \u003cp\u003e16.2 Kuhn–Tucker Characterization of the Log-Optimal Portfolio 617\u003c\/p\u003e \u003cp\u003e16.3 Asymptotic Optimality of the Log-Optimal Portfolio 619\u003c\/p\u003e \u003cp\u003e16.4 Side Information and the Growth Rate 621\u003c\/p\u003e \u003cp\u003e16.5 Investment in Stationary Markets 623\u003c\/p\u003e \u003cp\u003e16.6 Competitive Optimality of the Log-Optimal Portfolio 627\u003c\/p\u003e \u003cp\u003e16.7 Universal Portfolios 629\u003c\/p\u003e \u003cp\u003e16.7.1 Finite-Horizon Universal Portfolios 631\u003c\/p\u003e \u003cp\u003e16.7.2 Horizon-Free Universal Portfolios 638\u003c\/p\u003e \u003cp\u003e16.8 Shannon–McMillan–Breiman Theorem (General AEP) 644\u003c\/p\u003e \u003cp\u003eSummary 650\u003c\/p\u003e \u003cp\u003eProblems 652\u003c\/p\u003e \u003cp\u003eHistorical Notes 655\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Inequalities in Information Theory 657\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Basic Inequalities of Information Theory 657\u003c\/p\u003e \u003cp\u003e17.2 Differential Entropy 660\u003c\/p\u003e \u003cp\u003e17.3 Bounds on Entropy and Relative Entropy 663\u003c\/p\u003e \u003cp\u003e17.4 Inequalities for Types 665\u003c\/p\u003e \u003cp\u003e17.5 Combinatorial Bounds on Entropy 666\u003c\/p\u003e \u003cp\u003e17.6 Entropy Rates of Subsets 667\u003c\/p\u003e \u003cp\u003e17.7 Entropy and Fisher Information 671\u003c\/p\u003e \u003cp\u003e17.8 Entropy Power Inequality and Brunn–Minkowski Inequality 674\u003c\/p\u003e \u003cp\u003e17.9 Inequalities for Determinants 679\u003c\/p\u003e \u003cp\u003e17.10 Inequalities for Ratios of Determinants 683\u003c\/p\u003e \u003cp\u003eSummary 686\u003c\/p\u003e \u003cp\u003eProblems 686\u003c\/p\u003e \u003cp\u003eHistorical Notes 687\u003c\/p\u003e \u003cp\u003eBibliography 689\u003c\/p\u003e \u003cp\u003eList of Symbols 723\u003c\/p\u003e \u003cp\u003eIndex 727\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Interscience","offers":[{"title":"Brand 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