{"title":"Information theory Books","description":"","products":[{"product_id":"distant-horizons-9780226612836","title":"Distant Horizons","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Distant Horizons not only proves that Ted Underwood is defining the field of cultural analytics as it emerges; it shows us why. Combining literary theory with a deep understanding of computational methods, this volume demonstrates and effectively argues that quantitative analysis is best used not to find objective truths but to explore perspectives, both historically local and theoretical. It is at once a primer for quantitative literacy and a historically sensitive exploration of gender, genre, character, and audience, putting paid once and for all to the notion that statistical methods have no place in hermeneutics.\"--Laura Mandell, author of Breaking the Book: Print Humanities in the Digital Age \"Distant Horizons is of compelling interest to digital humanists. But its true audience is a wider society of literary and other humanities scholars spanning across fields, periods, approaches, and levels. For this larger audience, Ted Underwood goes out of his way to make distant reading accessible, inviting, and persuasive. This innovative book is the breakout work digital humanists have been waiting for, and it is positioned to be a landmark work in literary scholarship at large.\"--Alan Liu, author of Friending the Past: The Sense of History in the Digital Age","brand":"The University of Chicago Press","offers":[{"title":"Default Title","offer_id":48732913336663,"sku":"9780226612836","price":22.8,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780226612836.jpg?v=1719998925"},{"product_id":"inference-and-learning-from-data-volume-2-9781009218269","title":"Inference and Learning from Data Volume 2","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this te\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Inference and Learning from Data is a uniquely comprehensive introduction to the signal processing foundations of modern data science. Lucidly written, with a carefully balanced choice of topics, this textbook is an indispensable resource for both graduate students and data science practitioners, a piece of lasting value.' Helmut Bölcskei, ETH Zurich\u003cbr\u003e'This textbook provides a lucid and magisterial treatment of methods for inference and learning from data, aided by hundreds of solved examples, computer simulations, and over 1000 problems. The material ranges from fundamentals to recent advances in statistical learning theory; variational inference; neural, convolutional, and Bayesian networks; and several other topics. It is aimed at students and practitioners, and can be used for several different introductory and advanced courses.' Thomas Kailath, Stanford University\u003cbr\u003e'A tour de force comprehensive three-volume set for the fast-developing areas of data science, machine learning, and statistical signal processing. With masterful clarity and depth, Sayed covers, connects, and integrates background fundamentals and classical and emerging methods in inference and learning. The books are rich in worked-out examples, exercises, and links to data sets. Commentaries with historical background and contexts for the topics covered in each chapter are a special feature.' Mostafa Kaveh, University of Minnesota\u003cbr\u003e'This is the first of a three-volume series covering from fundamentals to the many various methods in inference and learning from data. Professor Sayed is a prolific author of award-winning books and research papers who has himself contributed significantly to many of the topics included in the series. With his encyclopedic knowledge, his careful attention to detail, and in a very approachable style, this first volume covers the basics of matrix theory, probability and stochastic processes, convex and non-convex optimization, gradient-descent, convergence analysis, and several other advanced topics that will be needed for volume II (Inference) and volume III (Learning). This series, and in particular this volume, will be a must-have for educators, students, researchers, and technologists alike who are pursuing a systematic study, want a quick refresh, or may use it as a helpful reference to learn about these fundamentals.' Jose Moura, Carnegie Mellon University\u003cbr\u003e'Volume I of Inference and Learning from Data provides a foundational treatment of one of the most topical aspects of contemporary signal and information processing, written by one of the most talented expositors in the field. It is a valuable resource both as a textbook for students wishing to enter the field and as a reference work for practicing engineers.' Vincent Poor, Princeton University\u003cbr\u003e'Inference and Learning from Data, Vol. I: Foundations offers an insightful and well-integrated primer with just the right balance of everything that new graduate students need to put their research on a solid footing. It covers foundations in a modern way - emphasizing the most useful concepts, including proofs, and timely topics which are often missing from introductory graduate texts. All in one beautifully written textbook. An impressive feat! I highly recommend it.' Nikolaos Sidiropoulos, University of Virginia\u003cbr\u003e'This exceptional encyclopedic work on learning from data will be the bible of the field for many years to come. Totaling more than 3000 pages, this three-volume book covers in an exhaustive and timely manner the topic of data science, which has become critically important to many areas and lies at the basis of modern signal processing, machine learning, artificial intelligence, and their numerous applications. Written by an authority in the field, the book is really unique in scale and breadth, and it will be an invaluable source of information for students, researchers, and practitioners alike.' Peter Stoica, Uppsala University\u003cbr\u003e'Very meticulous, thorough, and timely. This volume is largely focused on optimization, which is so important in the modern-day world of data science, signal processing, and machine learning. The book is classical and modern at the same time - many classical topics are nicely linked to modern topics of current interest. All the necessary mathematical background is covered. Professor Sayed is one of the foremost researchers and educators in the field and the writing style is unhurried and clear with many examples, truly reflecting the towering scholar that he is. This volume is so complete that it can be used for self-study, as a classroom text, and as a timeless research reference.' P. P. Vaidyanathan, Caltech\u003cbr\u003e'The book series is timely and indispensable. It is a unique companion for graduate students and early-career researchers. The three volumes provide an extraordinary breadth and depth of techniques and tools, and encapsulate the experience and expertise of a world-class expert in the field. The pedagogically crafted text is written lucidly, yet never compromises rigor. Theoretical concepts are enhanced with illustrative figures, well-thought problems, intuitive examples, datasets, and MATLAB codes that reinforce readers' learning.' Abdelhak Zoubir, TU Darmstadt\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; Notation; 27. Mean-Square-Error inference; 28. Bayesian inference; 29. Linear regression; 30. Kalman filter; 31. Maximum likelihood; 32. Expectation maximization; 33. Predictive modeling; 34. Expectation propagation; 35. Particle filters; 36. Variational inference; 37. Latent Dirichlet allocation; 38. Hidden Markov models; 39. Decoding HMMs; 40. Independent component analysis; 41. Bayesian networks; 42. Inference over graphs; 43. Undirected graphs; 44. Markov decision processes; 45. Value and policy iterations; 46. Temporal difference learning; 47. Q-learning; 48. Value function approximation; 49. Policy gradient methods; Author index; Subject index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738019213655,"sku":"9781009218269","price":71.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781009218269.jpg?v=1723811682"},{"product_id":"introduction-to-digital-communications-9781009220811","title":"Introduction to Digital Communications","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMaster the fundamentals of digital communications systems with this accessible and hands-on introductory textbook, carefully interweaving theory and practice. The just-in-time approach introduces essential background as needed, keeping academic theory firmly linked to practical applications. The example-led teaching frames key concepts in the context of real-world systems, such as 5G, WiFi, and GPS. Stark provides foundational material on the trade-offs between energy and bandwidth efficiency, giving students a solid grounding in the fundamental challenges of designing digital communications systems. Features include over 300 illustrative figures, 80 examples, and 130 end-of-chapter problems to reinforce student understanding, with solutions for instructors. Accompanied online by lecture slides, computational MATLAB and Python resources, and supporting data sets, this is the ideal introduction to digital communications for senior undergraduate and graduate students in electrical engineering.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'This book emphasizes the fundamentals of digital communication as well as its practice. It provides examples to enhance the understanding, and the many illustrations explain the basic concepts very well. Several concepts from actual engineering practice are discussed in detail.' Ender Ayanoglu, University of California, Irvine\u003cbr\u003e'Wayne Stark is a widely respected researcher in digital communications, as well as a dedicated and talented teacher. This book reflects his years of experience teaching a challenging and rapidly changing subject to senior undergraduate and first-year graduate students. His choice of topics and careful balance between theory and practice ensure that this book will be a valuable resource in electrical engineering curricula for years to come.' Tom Fuja, University of Notre Dame\u003cbr\u003e'This self-contained book is excellent for a first course in digital communications. It strikes a perfect balance in theory, practice, and insights, so that a beginner can get a good understanding without getting lost in advanced mathematical concepts.' Sudharman K. Jayaweera, University of New Mexico\u003cbr\u003e'This is an extraordinary textbook on digital communication theory and practices. Key results are derived step by step, and it provides many examples and figures that help students grasp key concepts. I wish it had been available when I was a student.' Sang Wu Kim, Iowa State University\u003cbr\u003e'Not only is this textbook comprehensive and well written, it is mathematically rigorous. The specific numerical examples and practical applications enhance the theoretical derivations. The author does an excellent job of communicating the importance of each result, making it an appropriate textbook for senior undergraduates taking a solid course in the theory of digital communications.' Laurence B. Milstein, University of California, San Diego\u003cbr\u003e'I enjoyed this book's clarity and logical presentation. It is easy to read, balancing mathematical fundamentals with practical applications, problem sets, and examples. I'd be delighted to use it when teaching my undergraduate course on Communication Systems and Principles. This concise resource provides a thorough foundation on digital communication concepts, systems, and techniques, explaining communication systems in general and digital communications specifically.' Lina Mohjazi, University of Glasgow\u003cbr\u003e'The real jewel of the book is the introduction chapter. It lays out the most important design considerations and trade-offs at a high (but not superficial) level straightaway, serving as a roadmap to the material in the rest of the book. It is the best and most useful introduction chapter that no one should skip!' Tan F. Wong, University of Florida\u003cbr\u003e'This is an excellent textbook for students, communications engineers, and researchers alike. Based on many years' teaching experience, it includes detailed and illustrative examples that help students understand the fundamentals of digital communications. Professor Stark explains the trade-offs of different key parameters in digital communications, and covers state-of-the-art technologies such as LDPC codes. Each chapter contains clear goals, summaries, and useful exercises.' Xiang-Gen Xia, University of Delaware\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eContents; Preface; Acknowledgement; List of abbreviations; 1. Fundamentals of digital communications; 2. Modulation and demodulation; 3. Probability, random variables, random processes, signal bandwidth; 4. Error probability for binary signals; 5. Optimal receivers for M-ary communication; 6. Modulation techniques; 7. Wireless channels and transmission techniques; 8. Block codes; 9. Convolutional codes; Appendix A. Pseudorandom sequences; Appendix B. Trigonometric and fourier transform iIdentities; Appendix C. Finite fields and BCH codes; Appendix D. Simulation of signals and noise; References; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738019639639,"sku":"9781009220811","price":71.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781009220811.jpg?v=1723811684"},{"product_id":"inference-and-learning-from-data-volume-1-9781009218122","title":"Inference and Learning from Data Volume 1","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWritten in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to core topics in inference and learning. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning, and engineering.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Inference and Learning from Data is a uniquely comprehensive introduction to the signal processing foundations of modern data science. Lucidly written, with a carefully balanced choice of topics, this textbook is an indispensable resource for both graduate students and data science practitioners, a piece of lasting value.' Helmut Bölcskei, ETH Zurich\u003cbr\u003e'This textbook provides a lucid and magisterial treatment of methods for inference and learning from data, aided by hundreds of solved examples, computer simulations, and over 1000 problems. The material ranges from fundamentals to recent advances in statistical learning theory; variational inference; neural, convolutional, and Bayesian networks; and several other topics. It is aimed at students and practitioners, and can be used for several different introductory and advanced courses.' Thomas Kailath, Stanford University\u003cbr\u003e'A tour de force comprehensive three-volume set for the fast-developing areas of data science, machine learning, and statistical signal processing. With masterful clarity and depth, Sayed covers, connects, and integrates background fundamentals and classical and emerging methods in inference and learning. The books are rich in worked-out examples, exercises, and links to data sets. Commentaries with historical background and contexts for the topics covered in each chapter are a special feature.' Mostafa Kaveh, University of Minnesota\u003cbr\u003e'This is the first of a three-volume series covering from fundamentals to the many various methods in inference and learning from data. Professor Sayed is a prolific author of award-winning books and research papers who has himself contributed significantly to many of the topics included in the series. With his encyclopedic knowledge, his careful attention to detail, and in a very approachable style, this first volume covers the basics of matrix theory, probability and stochastic processes, convex and non-convex optimization, gradient-descent, convergence analysis, and several other advanced topics that will be needed for volume II (Inference) and volume III (Learning). This series, and in particular this volume, will be a must-have for educators, students, researchers, and technologists alike who are pursuing a systematic study, want a quick refresh, or may use it as a helpful reference to learn about these fundamentals.' Jose Moura, Carnegie Mellon University\u003cbr\u003e'Volume I of Inference and Learning from Data provides a foundational treatment of one of the most topical aspects of contemporary signal and information processing, written by one of the most talented expositors in the field. It is a valuable resource both as a textbook for students wishing to enter the field and as a reference work for practicing engineers.' Vincent Poor, Princeton University\u003cbr\u003e'Inference and Learning from Data, Vol. I: Foundations offers an insightful and well-integrated primer with just the right balance of everything that new graduate students need to put their research on a solid footing. It covers foundations in a modern way - emphasizing the most useful concepts, including proofs, and timely topics which are often missing from introductory graduate texts. All in one beautifully written textbook. An impressive feat! I highly recommend it.' Nikolaos Sidiropoulos, University of Virginia\u003cbr\u003e'This exceptional encyclopedic work on learning from data will be the bible of the field for many years to come. Totaling more than 3000 pages, this three-volume book covers in an exhaustive and timely manner the topic of data science, which has become critically important to many areas and lies at the basis of modern signal processing, machine learning, artificial intelligence, and their numerous applications. Written by an authority in the field, the book is really unique in scale and breadth, and it will be an invaluable source of information for students, researchers, and practitioners alike.' Peter Stoica, Uppsala University\u003cbr\u003e'Very meticulous, thorough, and timely. This volume is largely focused on optimization, which is so important in the modern-day world of data science, signal processing, and machine learning. The book is classical and modern at the same time - many classical topics are nicely linked to modern topics of current interest. All the necessary mathematical background is covered. Professor Sayed is one of the foremost researchers and educators in the field and the writing style is unhurried and clear with many examples, truly reflecting the towering scholar that he is. This volume is so complete that it can be used for self-study, as a classroom text, and as a timeless research reference.' P. P. Vaidyanathan, Caltech\u003cbr\u003e'The book series is timely and indispensable. It is a unique companion for graduate students and early-career researchers. The three volumes provide an extraordinary breadth and depth of techniques and tools, and encapsulate the experience and expertise of a world-class expert in the field. The pedagogically crafted text is written lucidly, yet never compromises rigor. Theoretical concepts are enhanced with illustrative figures, well-thought problems, intuitive examples, datasets, and MATLAB codes that reinforce readers' learning.' Abdelhak Zoubir, TU Darmstadt\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eContents; Preface; Notation; 1. Matrix theory; 2. Vector differentiation; 3. Random variables; 4. Gaussian distribution; 5. Exponential distributions; 6. Entropy and divergence; 7. Random processes; 8. Convex functions; 9. Convex optimization; 10. Lipschitz conditions; 11. Proximal operator; 12. Gradient descent method; 13. Conjugate gradient method; 14. Subgradient method; 15. Proximal and mirror descent methods; 16. Stochastic optimization; 17. Adaptive gradient methods; 18. Gradient noise; 19. Convergence analysis I: Stochastic gradient algorithms; 20. Convergence analysis II: Stochasic subgradient algorithms; 21: Convergence analysis III: Stochastic proximal algorithms; 22. Variance-reduced methods I: Uniform sampling; 23. Variance-reduced methods II: Random reshuffling; 24. Nonconvex optimization; 25. Decentralized optimization I: Primal methods; 26: Decentralized optimization II: Primal-dual methods; Author index; Subject index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738022850903,"sku":"9781009218122","price":80.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781009218122.jpg?v=1723811686"},{"product_id":"inference-and-learning-from-data-volume-3-9781009218283","title":"Inference and Learning from Data Volume 3","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Inference and Learning from Data is a uniquely comprehensive introduction to the signal processing foundations of modern data science. Lucidly written, with a carefully balanced choice of topics, this textbook is an indispensable resource for both graduate students and data science practitioners, a piece of lasting value.' Helmut Bölcskei, ETH Zurich\u003cbr\u003e'This textbook provides a lucid and magisterial treatment of methods for inference and learning from data, aided by hundreds of solved examples, computer simulations, and over 1000 problems. The material ranges from fundamentals to recent advances in statistical learning theory; variational inference; neural, convolutional, and Bayesian networks; and several other topics. It is aimed at students and practitioners, and can be used for several different introductory and advanced courses.' Thomas Kailath, Stanford University\u003cbr\u003e'A tour de force comprehensive three-volume set for the fast-developing areas of data science, machine learning, and statistical signal processing. With masterful clarity and depth, Sayed covers, connects, and integrates background fundamentals and classical and emerging methods in inference and learning. The books are rich in worked-out examples, exercises, and links to data sets. Commentaries with historical background and contexts for the topics covered in each chapter are a special feature.' Mostafa Kaveh, University of Minnesota\u003cbr\u003e'This is the first of a three-volume series covering from fundamentals to the many various methods in inference and learning from data. Professor Sayed is a prolific author of award-winning books and research papers who has himself contributed significantly to many of the topics included in the series. With his encyclopedic knowledge, his careful attention to detail, and in a very approachable style, this first volume covers the basics of matrix theory, probability and stochastic processes, convex and non-convex optimization, gradient-descent, convergence analysis, and several other advanced topics that will be needed for volume II (Inference) and volume III (Learning). This series, and in particular this volume, will be a must-have for educators, students, researchers, and technologists alike who are pursuing a systematic study, want a quick refresh, or may use it as a helpful reference to learn about these fundamentals.' Jose Moura, Carnegie Mellon University\u003cbr\u003e'Volume I of Inference and Learning from Data provides a foundational treatment of one of the most topical aspects of contemporary signal and information processing, written by one of the most talented expositors in the field. It is a valuable resource both as a textbook for students wishing to enter the field and as a reference work for practicing engineers.' Vincent Poor, Princeton University\u003cbr\u003e'Inference and Learning from Data, Vol. I: Foundations offers an insightful and well-integrated primer with just the right balance of everything that new graduate students need to put their research on a solid footing. It covers foundations in a modern way - emphasizing the most useful concepts, including proofs, and timely topics which are often missing from introductory graduate texts. All in one beautifully written textbook. An impressive feat! I highly recommend it.' Nikolaos Sidiropoulos, University of Virginia\u003cbr\u003e'This exceptional encyclopedic work on learning from data will be the bible of the field for many years to come. Totaling more than 3000 pages, this three-volume book covers in an exhaustive and timely manner the topic of data science, which has become critically important to many areas and lies at the basis of modern signal processing, machine learning, artificial intelligence, and their numerous applications. Written by an authority in the field, the book is really unique in scale and breadth, and it will be an invaluable source of information for students, researchers, and practitioners alike.' Peter Stoica, Uppsala University\u003cbr\u003e'Very meticulous, thorough, and timely. This volume is largely focused on optimization, which is so important in the modern-day world of data science, signal processing, and machine learning. The book is classical and modern at the same time - many classical topics are nicely linked to modern topics of current interest. All the necessary mathematical background is covered. Professor Sayed is one of the foremost researchers and educators in the field and the writing style is unhurried and clear with many examples, truly reflecting the towering scholar that he is. This volume is so complete that it can be used for self-study, as a classroom text, and as a timeless research reference.' P. P. Vaidyanathan, Caltech\u003cbr\u003e'The book series is timely and indispensable. It is a unique companion for graduate students and early-career researchers. The three volumes provide an extraordinary breadth and depth of techniques and tools, and encapsulate the experience and expertise of a world-class expert in the field. The pedagogically crafted text is written lucidly, yet never compromises rigor. Theoretical concepts are enhanced with illustrative figures, well-thought problems, intuitive examples, datasets, and MATLAB codes that reinforce readers' learning.' Abdelhak Zoubir, TU Darmstadt\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; Notation; 50. Least-squares problems; 51. Regularization; 52. Nearest-neighbor rule; 53. Self-organizing maps; 54. Decision trees; 55. Naive Bayes classifier; 56. Linear discriminant analysis; 57. Principal component analysis; 58. Dictionary learning; 59. Logistic regression; 60. Perceptron; 61. Support vector machines; 62. Bagging and boosting; 63. Kernel methods; 64. Generalization theory; 65. Feedforward neural networks; 66. Deep belief networks; 67. Convolutional networks; 68. Generative networks; 69. Recurrent networks; 70. Explainable learning; 71. Adversarial attacks; 72. Meta learning; Author index; Subject index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738022949207,"sku":"9781009218283","price":71.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781009218283.jpg?v=1723811686"},{"product_id":"why-dna-9781107697522","title":"Why DNA","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eInformation is central to the evolution of biological complexity, a physical system relying on a continuous supply of energy. Biology provides superb examples of the consequent Darwinian selection of mechanisms for efficient energy utilisation. Genetic information, underpinned by the Watson-Crick base-pairing rules is largely encoded by DNA, a molecule uniquely adapted to its roles in information storage and utilisation.This volume addresses two fundamental questions. Firstly, what properties of the molecule have enabled it to become the predominant genetic material in the biological world today and secondly, to what extent have the informational properties of the molecule contributed to the expansion of biological diversity and the stability of ecosystems. The author argues that bringing these two seemingly unrelated topics together enables Schrödinger''s What is Life?, published before the structure of DNA was known, to be revisited and his ideas examined in the context of our curren\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'The essence of the book is in its title. The DNA structures and topology are described so clearly that the reader perceives these intricacies as pure evolutionary elegance, and understands WHY it is only in its balance of stability and agility that life could have started its journey. This book explains how DNA has become the fascinating prism, made of a fabric of complexity and information, into which the living reflects itself. My opinion is passionate because I have been thinking about the same problems for decades, and here I find many of the answers. Especially: what makes DNA so unique? It is a text that I keep reading over again.' Ernesto Di Mauro, IBPM, National Research Council, Rome\u003cbr\u003e'In What Is Life? Schrödinger conjectured that, in animate matter, order is derived from order, foreshadowing the discovery of DNA structure. Why DNA? is about this molecule and its dual information content - in linear genetic code and in thermodynamics of three-dimensional DNA structures. It addresses how DNA's intrinsic order led to complex, highly ordered living organisms, in a world that strives towards disorder. Why would DNA supplant RNA in carrying hereditary information during biological evolution? Why did multicellular organisms emerge, since natural selection favours the fittest, such as simple bacteria? What is complexity, and what has it to do with Bayesian logic? How do complexity, information and energy interrelate? This is a succinct discourse on Schrödinger's question, expanding from molecular interactions and genome cooperation to ecological systems and societal evolution. A must-read for biology scholars, and anyone interested in life's origins, biological evolution and the interface of biology and physics.' Georgi Muskhelishvili, Agricultural University of Georgia, Tbilisi\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eAcknowledgements; Preface; 1. The perennial question; 2. The nature of information – information, complexity and entropy; 3. DNA – the molecule; 4. The evolution of biological complexity; 5. Cooperating genomes; 6. DNA, information and complexity; 7. Origins; 8. The complexity of societies; 9. Why DNA – and not RNA?; General reading and bibliography.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738278179159,"sku":"9781107697522","price":20.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781107697522.jpg?v=1723811884"},{"product_id":"machine-learning-refined-9781108480727","title":"Machine Learning Refined","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWith its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for grad\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'An excellent book that treats the fundamentals of machine learning from basic principles to practical implementation. The book is suitable as a text for senior-level and first-year graduate courses in engineering and computer science. It is well organized and covers basic concepts and algorithms in mathematical optimization methods, linear learning, and nonlinear learning techniques. The book is nicely illustrated in multiple colors and contains numerous examples and coding exercises using Python.' John G. Proakis, University of California, San Diego\u003cbr\u003e'Some machine learning books cover only programming aspects, often relying on outdated software tools; some focus exclusively on neural networks; others, solely on theoretical foundations; and yet more books detail advanced topics for the specialist. This fully revised and expanded text provides a broad and accessible introduction to machine learning for engineering and computer science students. The presentation builds on first principles and geometric intuition, while offering real-world examples, commented implementations in Python, and computational exercises. I expect this book to become a key resource for students and researchers.' Osvaldo Simeone, Kings College London\u003cbr\u003e'This book is great for getting started in machine learning. It builds up the tools of the trade from first principles, provides lots of examples, and explains one thing at a time at a steady pace. The level of detail and runnable code show what's really going when we run a learning algorithm.' David Duvenaud, University of Toronto\u003cbr\u003e'This book covers various essential machine learning methods (e.g., regression, classification, clustering, dimensionality reduction, and deep learning) from a unified mathematical perspective of seeking the optimal model parameters that minimize a cost function. Every method is explained in a comprehensive, intuitive way, and mathematical understanding is aided and enhanced with many geometric illustrations and elegant Python implementations.' Kimiaki Sihrahama, Kindai University, Japan\u003cbr\u003e'Books featuring machine learning are many, but those which are simple, intuitive, and yet theoretical are extraordinary 'outliers'. This book is a fantastic and easy way to launch yourself into the exciting world of machine learning, grasp its core concepts, and code them up in Python or Matlab. It was my inspiring guide in preparing my 'Machine Learning Blinks' on my BASIRA YouTube channel for both undergraduate and graduate levels.' Islem Rekik, Director of the Brain And SIgnal Research and Analysis (BASIRA) Laboratory\u003cbr\u003e'With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.' politcommerce.com\u003cbr\u003e'This is a comprehensive textbook on the fundamental concepts of machine learning. In the second edition, the authors provide a very accessible introduction to the main ideas behind machine learning models.' Helena Mihaljević, zbMATH\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction to machine learning; Part I. Mathematical Optimization: 2. Zero order optimization techniques; 3. First order methods; 4. Second order optimization techniques; Part II. Linear Learning: 5. Linear regression; 6. Linear two-class classification; 7. Linear multi-class classification; 8. Linear unsupervised learning; 9. Feature engineering and selection; Part III. Nonlinear Learning: 10. Principles of nonlinear feature engineering; 11. Principles of feature learning; 12. Kernel methods; 13. Fully-connected neural networks; 14. Tree-based learners; Part IV. Appendices: Appendix A. Advanced first and second order optimization methods; Appendix B. Derivatives and automatic differentiation; Appendix C. Linear algebra.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738306556247,"sku":"9781108480727","price":55.09,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108480727.jpg?v=1723811911"},{"product_id":"model-checking-quantum-systems-9781108484305","title":"Model Checking Quantum Systems","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eModel checking is one of the most successful verification techniques and has been widely adopted in traditional computing and communication hardware and software industries. This book provides the first systematic introduction to model checking techniques applicable to quantum systems, with broad potential applications in the emerging industry of quantum computing and quantum communication as well as quantum physics. Suitable for use as a course textbook and for self-study, graduate and senior undergraduate students will appreciate the step-by-step explanations and the exercises included. Researchers and engineers in the related fields can further develop these techniques in their own work, with the final chapter outlining potential future applications.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'This book gives a thorough account of the principles of model checking for quantum systems. It covers the basics of verifying qualitative properties such as reachability as well as quantitative properties on quantum Markov chains. This is the first comprehensive work on this young and exciting research field.' Joost-Pieter Katoen, RWTH Aachen University\u003cbr\u003e'The authors have been, from the start of the quantum computer science endeavour, at the forefront of research in logical methods for quantum computing. This book provides the best possible introduction to quantum model checking, by the pioneers of the field. Bob Coecke, University of Oxford\u003cbr\u003e'A brief final chapter offering conclusions and future prospects will be of wider interest. This work is intended as an introduction for researchers entering the field of quantum computing, and is suitable as a textbook for physics or computer science graduate students … Recommended.' M. C. Ogilvie, Choice Magazine\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction; 2. Basics of Model Checking; 3. Basics of Quantum Theory; 4. Model Checking; 5. Model Checking Quantum Markov Chains; 6. Model Checking Super-operator-valued Markov Chains; 7. Conclusions and Prospects.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738308686167,"sku":"9781108484305","price":53.19,"currency_code":"GBP","in_stock":true}]},{"product_id":"the-quantum-internet-9781108491457","title":"The Quantum Internet","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA highly interdisciplinary overview of the emerging topic of the Quantum Internet. Current and future quantum technologies are covered in detail, in addition to their global socio-economic impact. Written in an engaging style and accessible to graduate students in physics, engineering, computer science and mathematics.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'This book explores the technical and socioeconomic aspects of a future quantum internet … The volume will be a valuable acquisition for any institution supporting research in quantum computing or, more broadly, the emerging science and engineering of quantum information … Highly recommended.' M. C. Ogilvie, Choice Connect\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I. Introduction: 1. Foreword; 2. Introduction. Part II. Classical Networks: 3. Mathematical representation of networks; 4. Network topologies; 5. Network algorithms. Part III. Quantum Networks: 6. Quantum channels; 7. Optical encoding of quantum information; 8. Errors in quantum networks; 9. Quantum cost vector analysis; 10. Routing strategies; 11. Interconnecting and interfacing quantum networks; 12. Optical routers; 13. Optical stability in quantum networks. Part IV. Protocols for the Quantum Internet: 14. State preparation; 15. Measurement; 16. Evolution; 17. High-level protocols. Part V. Entanglement Distribution: 18. Entanglement – The ultimate quantum resource; 19. Quantum repeater networks; 20. The irrelevance of latency; 21. The quantum Sneakernet™. Part VI. Quantum Cryptography: 22. What is security?; 23. Classical cryptography; 24. Attacks on classical cryptography; 25. Bitcoin and the blockchain; 26. Quantum cryptography; 27. Attacks on quantum cryptography. Part VII. Quantum Computing: 28. Models for quantum computation; 29. Quantum algorithms. Part VIII. Cloud Quantum Computing: 30. The Quantum Cloud™; 31. Encrypted cloud quantum computation. Part IX. Economics and Politics: 32. Classical-equivalent computational power and computational scaling functions; 33. Per-qubit computational power; 34. Time-sharing; 35. Economic model assumptions; 36. Network power; 37. Network value; 38. Rate of return; 39. Market competitiveness; 40. Cost of computation; 41. Arbitrage-free time-sharing model; 42. Problem size scaling functions; 43. Quantum computational leverage; 44. Static computational return; 45. Forward contract pricing model; 46. Political leverage; 47. Economic properties of the qubit marketplace; 48. Economic implications; 49. Game theory of the qubit marketplace. Part X. Essays: 50. The era of quantum supremacy; 51. The global virtual quantum computer; 52. The economics of the quantum internet; 53. Security implications of the global quantum internet; 54. Geostrategic quantum politics; 55. The quantum ecosystem. Part XI. The End: 56. Conclusion. References. Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738311635287,"sku":"9781108491457","price":49.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108491457.jpg?v=1723811915"},{"product_id":"an-introduction-to-symbolic-dynamics-and-coding-9781108820288","title":"An Introduction to Symbolic Dynamics and Coding","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eSymbolic dynamics is a mature yet rapidly developing area of dynamical systems. It has established strong connections with many areas, including linear algebra, graph theory, probability, group theory, and the theory of computation, as well as data storage, statistical mechanics, and $C^*$-algebras. This Second Edition maintains the introductory character of the original 1995 edition as a general textbook on symbolic dynamics and its applications to coding. It is written at an elementary level and aimed at students, well-established researchers, and experts in mathematics, electrical engineering, and computer science. Topics are carefully developed and motivated with many illustrative examples. There are more than 500 exercises to test the reader''s understanding. In addition to a chapter in the First Edition on advanced topics and a comprehensive bibliography, the Second Edition includes a detailed Addendum, with companion bibliography, describing major developments and new research d\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Shift spaces; 2. Shifts of finite type; 3. Sofic shifts; 4. Entropy; 5. Finite-state codes; 6. Shifts as dynamical systems; 7. Conjugacy; 8. Finite-to-one codes and finite equivalence; 9. Degrees of codes and almost conjugacy; 10. Embeddings and factor codes; 11. Realization; 12. Equal entropy factors; 13. Guide to advanced topics; Addendum for the second edition; Bibliography; Addendum bibliography; Notation index; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738335424855,"sku":"9781108820288","price":60.1,"currency_code":"GBP","in_stock":true}]},{"product_id":"time-series-for-data-scientists-9781108837774","title":"Time Series for Data Scientists","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eLearn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book''s companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplines\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'This book provides an excellent introduction to time series modelling and forecasting which are increasingly important tools in the domain of official statistics. The clear descriptions and real-life examples provided in this text make it easy to digest for those not already familiar with the topic. In addition, the exercises allow readers to develop their understanding in more depth through hands-on applications of the methods to real data using open-source tools. The inclusion of modern topics such as machine learning and artificial intelligence are a valuable addition to make the text relevant and comprehensive.' Steve Matthews, Statistics Canada\u003cbr\u003e'This book is a great introduction to the ideas and methods of time series data analysis. Chapter by chapter, it will show you its most valuable features, like the wealth of real examples as well as practical uses of R and graphical visualization. You will certainly enjoy this text, as it is suitable for a wide range of statistical courses.' Vera Ioudina, Texas State University\u003cbr\u003e'Lots of good real world examples together with the use of R helps a lot as do the nice set of exercises. In time series, it is a tricky balance between overdoing theory or just hand waving and here the author does very well. This would make a lovely course text!' Gareth Janacek, University of East Anglia\u003cbr\u003e'Time Series for Data Scientists' develops your intuition before walking through classical and modern time series methods in easy-to-understand terms. With each algorithm Dr. Sanchez first helps you understand the motivation behind the approach; then walks you through the formulas step-by-step, outlining what we're doing and why; she also includes R code to help you apply the techniques learned to solve real-world business problems using real-world data sets; and takes the time to show you how to interpret the output, and discuss what to try next when an initial approach doesn't quite match the trends in the data. Whether you're an undergraduate or graduate student, are curious about time series methods, are looking for a self-paced book, or a reference guide, this is a must-have.' Irina Kukuyeva, Fractional Chief Data Officer\u003cbr\u003e'A fine textbook for an introductory time series course aimed at undergraduates in Statistics or Data Science. The author did an excellent work in the choice of topics, covering from classical exploratory techniques to modern machine learning approaches, while keeping the level of the exposition accessible to readers with a modicum of mathematical background. To be recommended!' Giovanni Petris, University of Arkansas\u003cbr\u003e'This book should be a serious contender if you are looking for an introductory text for an undergraduate course in time series. It is especially suited for a course populated with students having varying degrees of mathematical skill levels. Its conversational approach to introducing time series concepts and the use of insightful examples throughout the book makes it very accessible to students who are not highly trained in abstract mathematical reasoning. Nevertheless, it does not shy away from providing the theoretical underpinnings of various time series models but does so in a manner very accessible to students. The availability of R code throughout the book is an added plus. Even if I am teaching an upper-level graduate course in time series, I would use this book as a supplement simply because of the plethora of examples and data sources it provides.' V. A. Samaranayake, Missouri University of Science and Technology\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I. Descriptive Features of Time Series Data: 1. Introduction to time series data; 2. Smoothing and decomposing a time series; 3. Summary statistics of stationary time series; Part II. Univariate Models of Temporal Dependence: 4. The algebra of differencing and backshifting; 5. Stationary stochastic processes; 6. ARIMA(p,d,q)(P,D,Q)$_F$ modeling and forecasting; Part III. Multivariate Modeling and Forecasting: 7. Latent process models for time series; 8. Vector autoregression; 9. Classical regression with ARMA residuals; 10. Machine learning methods for time series; References; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738340864343,"sku":"9781108837774","price":56.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108837774.jpg?v=1723811949"},{"product_id":"machine-learning-fundamentals-9781108940023","title":"Machine Learning Fundamentals","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely from scratch based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Dr Jiang has done a superb job in covering many methods, both theoretical and practical, across a broad spectrum of machine learning in this timely book. I worked closely with Dr Jiang on Bayesian speech recognition during late 90's and I have personally witnessed his excellent skills in applying machine learning to solving a wide range of practical problems. In this book, Dr Jiang has expanded his scope into a much wider set of logically organized topics in modern machine learning. The organization of the material is highly unique and cogent. A number of hot topics in machine learning, including deep learning and neural networks, are naturally incorporated in the book, which not only provides sufficient technical depth for the readers but also aligns well with popular toolkits for implementing the related machine learning methods.' Li Deng, formerly of Microsoft Corporation and Citadel LLC\u003cbr\u003e'It is beautifully designed, with many color images that make the complex subject matter manageable … It is a book for students and developers who are committed to specializing in ML or a specific area of ​​it.' Karl van Heijster , De Leesclub van Alles\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction; 2. Mathematical Foundation; 3. Supervised Machine Learning (in a nutshell); 4. Feature Extraction; 5. Statistical Learning Theory; 6. Linear Models; 7. Learning Discriminative Models in General; 8. Neural Networks; 9. Ensemble Learning; 10. Overview of Generative Models; 11. Unimodal Models; 12. Mixture Models; 13. Entangled Models; 14. Bayesian Learning; 15. Graphical Models.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738346074455,"sku":"9781108940023","price":40.84,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108940023.jpg?v=1723811956"},{"product_id":"the-fundamentals-of-heavy-tails-9781316511732","title":"The Fundamentals of Heavy Tails","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eHeavy tails extreme events or values more common than expected emerge everywhere: the economy, natural events, and social and information networks are just a few examples. Yet after decades of progress, they are still treated as mysterious, surprising, and even controversial, primarily because the necessary mathematical models and statistical methods are not widely known. This book, for the first time, provides a rigorous introduction to heavy-tailed distributions accessible to anyone who knows elementary probability. It tackles and tames the zoo of terminology for models and properties, demystifying topics such as the generalized central limit theorem and regular variation. It tracks the natural emergence of heavy-tailed distributions from a wide variety of general processes, building intuition. And it reveals the controversy surrounding heavy tails to be the result of flawed statistics, then equips readers to identify and estimate with confidence. Over 100 exercises complete this eng\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Heavy tailed distributions are ubiquitous in many disciplines which use probabilistic models. The book by Nair, Wierman and Zwart is a superb introduction to the topic and presents fundamental principles in a rigorous yet accessible manner. It is a must-read for researchers interested in understanding heavy tails.' R. Srikant, University of Illinois at Urbana-Champaign\u003cbr\u003e'As one of the people who keeps discovering heavy tails in computer systems, I'm thrilled to see a book that delves into the deeper foundations behind these ubiquitous distributions. This beautifully written book is both mathematically precise and also full of intuitions and examples which make it accessible to newcomers in the field.' Mor Harchol-Balter, Carnegie Mellon University\u003cbr\u003e'The book provides a fresh look at heavy-tailed probability distributions on the real line and their role in applied probability. The authors show that these distributions appear via natural algebraic operations. Their approach, towards understanding properties of these distributions, combines the key mathematical ideas alongside with informal explanations. Physical intuition is also provided, for example, the 'catastrophe\/big jump principle' for heavy-tailed distributions versus the 'conspiracy principle' for light-tailed ones. The book is designed to help the practitioner and includes many interesting examples and exercises that may help to the reader to adjust and enjoy its content.' Sergey Foss, Heriot-Watt University\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eCommonly used notation; 1. Introduction; Part I. Properties: 2. Scale invariance, power laws, and regular variation; 3. Catastrophes, conspiracies, and subexponential distributions; 4. Residual lives, hazard rates, and long tails; Part II. Emergence: 5. Additive processes; 6. Multiplicative processes; 7. Extremal processes; Part III. Estimation: 8. Estimating power-law distributions: Listen to the body; 9. Estimating power-law tails: Let the tail do the talking; References; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738560213335,"sku":"9781316511732","price":58.12,"currency_code":"GBP","in_stock":true}]},{"product_id":"fundamentals-of-classical-and-modern-errorcorrecting-codes-9781316512623","title":"Fundamentals of Classical and Modern","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eUsing easy-to-follow mathematics, this textbook provides comprehensive coverage of block codes and techniques for reliable communications and data storage. It covers major code designs and constructions from geometric, algebraic, and graph-theoretic points of view, decoding algorithms, error control additive white Gaussian noise (AWGN) and erasure, and dataless recovery. It simplifies a highly mathematical subject to a level that can be understood and applied with a minimum background in mathematics, provides step-by-step explanation of all covered topics, both fundamental and advanced, and includes plenty of practical illustrative examples to assist understanding. Numerous homework problems are included to strengthen student comprehension of new and abstract concepts, and a solutions manual is available online for instructors. Modern developments, including polar codes, are also covered. An essential textbook for senior undergraduates and graduates taking introductory coding courses, \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'… masterfully provides a comprehensive treatment of both traditional codes as well as new and most promising coding families and decoding algorithms …' Bane Vasić, University of Arizona\u003cbr\u003e' an excellent, unique, and valuable contribution to the teaching of the subject.' Ian Blake, University of British Columbia\u003cbr\u003e'A highly readable introduction into the theory of block codes, including classical code constructions, an extensive treatment of LDPC codes, with emphasis on quasi-cyclic constructions, and an introduction to polar codes. Recommended for a beginning graduate course in coding, with enough material for either one or two semesters. Numerous examples and problems make the book very student friendly.' Daniel Costello, University of Notre Dame\u003cbr\u003e'The book truly explains these highly mathematical subjects to a level that can be accessed and applied with as little background in mathematics as possible. It provides step-by-step explanation of all covered topics, both more theoretical or applied, and includes sufficient illustrative examples to assist understanding.' Nikolay Yankov, zbMATH\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; Acknowledgments; 1. Coding for reliable digital information transmission and storage; 2. Some elements of modern algebra and graphs; 3. Linear block codes; 4.Binary cyclic codes; 5. BCH codes; 6. Nonbinary BCH codes and Reed-Solomon codes; 7. Finite geometries, cyclic finite geometry codes, and majority-logic decoding; 8. Reed-Muller codes; 9. Some coding techniques; 10. Correction of error-bursts and erasures; 11. Introduction to low-density parity-check codes; 12. Cyclic and quasi-cyclic LDPC codes on finite geometries; 13. Partial geometries and their associated QC-LDPC codes; 14. Quasi-cyclic LDPC codes based on finite fields; 15. Graph-theoretic LDPC codes; 16. Collective encoding and soft-decision decoding of cyclic codes of prime lengths in Galois Fourier transform domain; 17. Polar codes; Appendices.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738560901463,"sku":"9781316512623","price":71.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781316512623.jpg?v=1720049469"},{"product_id":"expert-oracle-rac-performance-diagnostics-and-tuning-9781430267096","title":"Expert Oracle RAC Performance Diagnostics and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cem\u003eExpert Oracle RAC Performance Diagnostics and Tuning\u003c\/em\u003e provides comprehensive coverage of the features, technology and principles for testing and tuning RAC databases. The book takes a deep look at optimizing RAC databases by following a methodical approach based on scientific analysis rather than using a speculative approach, twisting and turning knobs and gambling on the system.\u003c\/p\u003e\u003cp\u003eThe book starts with the basic concepts of tuning methodology, capacity planning, and architecture. Author \u003cstrong\u003eMurali Vallath\u003c\/strong\u003e then dissects the various tiers of the testing implementation, including the operating system, the network, the application, the storage, the instance, the database, and the grid infrastructure. He also introduces tools for performance optimization and thoroughly covers each aspect of the tuning process, using many real-world examples, analyses, and solutions from the field that provide you with a solid, practical, and replicable approach to tuning a RAC en\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Methodology\u003c\/p\u003e\u003cp\u003e2. 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The SQL Scripts Used in This Book\u003c\/p\u003e\u003cp\u003eBibliography\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer-Verlag Berlin and Heidelberg GmbH \u0026 Co. 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This book is the first text to provide an information science perspective on IR. \u003cbr\u003e Unique in its scope, the book covers the whole spectrum of information retrieval, including:\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003ehistory and background\u003c\/li\u003e\n\u003cli\u003einformation behaviour and seeking\u003c\/li\u003e\n\u003cli\u003etask-based information searching and retrieval\u003c\/li\u003e\n\u003cli\u003eapproaches to investigating information interaction and\u003c\/li\u003e\n\u003cli\u003ebehaviour\u003c\/li\u003e\n\u003cli\u003einformation representation\u003c\/li\u003e\n\u003cli\u003eaccess models\u003c\/li\u003e\n\u003cli\u003eevaluation\u003c\/li\u003e\n\u003cli\u003einterfaces for IR\u003c\/li\u003e\n\u003cli\u003einteractive techniques\u003c\/li\u003e\n\u003cli\u003eweb retrieval, ranking and personalization\u003c\/li\u003e\n\u003cli\u003erecommendation, collaboration and social search\u003c\/li\u003e\n\u003cli\u003emultimedia: interfaces and access. \u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eReadership\u003c\/b\u003e: Senior undergraduates and masters’ level students of all information and library studies courses and practising LIS professionals who need to better appreciate how IR systems are designed, implemented and evaluated.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003e\"This book is a must if one is a student or researcher new to information science and, in particular, to information retrieval (IR) interaction and multimedia research.\"\u003c\/b\u003e\u003c\/p\u003e -- Journal of the American Society for Information Science and Technology\u003cbr\u003e\u003cp\u003e\u003cb\u003e\"This is an interesting collection that deserves to be adopted as a key text within information science courses. Award-winning, internationally renowned editors have enticed a number of experts, some with industry experience, to provide high-quality contributions. The solid chapters discussing core fields that make up its coverage – information seeking, information behaviour,information retrieval – assure its place on reading lists. The editors have ensured new developments receive attention but not at the expense of the essentials of the fields.\"\u003c\/b\u003e\u003c\/p\u003e -- Journal of Information Literacy\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eForeword - \u003ci\u003eTefko Saracevic \u003c\/i\u003e 1. Interactive information retrieval: history and background - \u003ci\u003eColleen Cool\u003c\/i\u003e and \u003ci\u003eNicholas J. Belkin \u003c\/i\u003e \u003cbr\u003e 2. Information behavior and seeking -\u003ci\u003e Peiling Wang\u003c\/i\u003e 3. Task-based information searching and retrieval - \u003ci\u003eElaine G. Toms \u003c\/i\u003e \u003cbr\u003e 4. Approaches to investigating information interaction and behaviour - \u003ci\u003eRaya Fidel \u003c\/i\u003e \u003cbr\u003e 5. Information representation - \u003ci\u003eMark D. Smucker \u003c\/i\u003e \u003cbr\u003e 6. Access models - \u003ci\u003eEdie Rasmussen \u003c\/i\u003e \u003cbr\u003e 7. Evaluation - \u003ci\u003eKalervo Järvelin \u003c\/i\u003e \u003cbr\u003e 8. Interfaces for information retrieval - \u003ci\u003eMax Wilson \u003c\/i\u003e \u003cbr\u003e 9. Interactive techniques - \u003ci\u003eRyen W. White \u003c\/i\u003e 10. Web retrieval, ranking and personalization -\u003ci\u003e Jaime Teevan \u003c\/i\u003eand \u003ci\u003eSusan Dumais \u003c\/i\u003e \u003cbr\u003e 11. Recommendation, collaboration and social search - \u003ci\u003eDavid M. Nichols \u003c\/i\u003eand\u003ci\u003e Michael B. Twidale \u003c\/i\u003e \u003cbr\u003e 12. Multimedia: behaviour, interfaces and interaction - \u003ci\u003eHaiming Liu, Suzanne Little\u003c\/i\u003e and \u003ci\u003eStefan Rüger \u003c\/i\u003e \u003cbr\u003e 13. Multimedia: information representation and access - \u003ci\u003eSuzanne Little, Evan Brown\u003c\/i\u003e and\u003ci\u003e Stefan Rüger\u003c\/i\u003e\u003c\/p\u003e","brand":"Facet Publishing","offers":[{"title":"Default Title","offer_id":48888162025815,"sku":"9781856047074","price":65.0,"currency_code":"GBP","in_stock":false}]},{"product_id":"software-source-code-statistical-modeling-9783110703306","title":"Software Source Code: Statistical Modeling","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":48889047581015,"sku":"9783110703306","price":51.75,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783110703306.jpg?v=1722552433"},{"product_id":"the-evolution-of-biological-information-9780691241166","title":"The Evolution of Biological Information","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":49083561673047,"sku":"9780691241166","price":106.25,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691241166.jpg?v=1725549329"},{"product_id":"a-first-course-in-network-science-9781108471138","title":"A First Course in Network Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eNetworks are everywhere: networks of friends, transportation networks and the Web. Neurons in our brains and proteins within our bodies form networks that determine our intelligence and survival. This modern, accessible textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Students will develop important, practical skills and learn to write code for using networks in their areas of interest - even as they are just learning to program with Python. Extensive sets of tutorials and homework problems provide plenty of hands-on practice and longer programming tutorials online further enhance students'' programming skills. This intuitive and direct approach makes the book ideal for a first course, aimed at a wide audience without a strong background in mathematics or computing but with a desire to learn the fundamentals and applications of network science.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'A First Course in Network Science by Menczer, Fortunato, and Davis is an easy-to-follow introduction into network science. An accessible text by some of the best-known practitioners of the field, offering a wonderful place to start one's journey into this fascinating field, and its potential applications.' Albert-László Barabási, Dodge Distinguished Professor of Network Science, Northeastern University\u003cbr\u003e'… this textbook has finally allowed me to teach the ideal intro courses on network science, of interest to computer scientists as well as mathematicians, statisticians, economists, sociologists, and physicists.' Giancarlo Ruffo, Associate Professor of Computer Science, University of Torino\u003cbr\u003e'The book by Menczer, Fortunato, and Davis, A First Course in Network Science, is an amazing tour de force in bringing network science concepts to the layman. It is an extraordinary book with which to start thinking about networks that nowadays represent the linchpins of our world.' Alex Arenas, Universidad Rovira i Virgili\u003cbr\u003e'Buckle up! This book bounds ahead of the curve in teaching network science. Without formalism, but with remarkable clarity and insight, the authors use experiential learning to animate concepts, captivate students, and deliver skills for analyzing and simulating network data. This book will not only make students smarter, they will feel and act smarter.' Brian Uzzi, Northwestern University\u003cbr\u003e'If you are looking for a sophisticated yet introductory book on network analysis from a network science perspective, look no further. This is an excellent introduction that is also eminently practical, integrating exactly the right set of tools. I highly recommend it.' Stephen Borgatti, University of Kentucky\u003cbr\u003e'This is a book that truly takes in hand students from all backgrounds to discover the power of network science. It guides the readers through the basic concepts needed to enter the field, while providing at the same time the necessary programming rudiments and tools. Rigorous, albeit very accessible, this book is the ideal starting point for any student fascinated by the emerging field of network science.' Alessandro Vespignani, Northeastern University\u003cbr\u003e'We cannot make sense of the world without learning about networks. This comprehensive and yet accessible text is an essential resource for all interested in mastering the basics of network science. Indispensable for undergraduate and graduate education, the book is also a much-needed primer for researchers across the many disciplines where networks are on the rise.' Olaf Sporns, Indiana University\u003cbr\u003e'This is a timely book that comes from authorities in the field of Complex Networks. The book is very well written and represents the state of the art of research in the field. For these reasons, it represents both a reference guide for experts and a great textbook for the students.' Guido Caldarelli, Scuola IMT Alti Studi Lucca\u003cbr\u003e'Should be titled the 'Joy of Networks', clearly conveys the fun and power of the science of networks, while providing extensive hands-on exercises with network data.' David Lazer, University Distinguished Professor of Political Science and Computer and Information Science, Northeastern University\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; Introduction; 1. Network elements; 2. Small worlds; 3. Hubs; 4. Directions and weights; 5. Network models; 6. Communities; 7. Dynamics; Appendix A. Python tutorial; Appendix B. NetLogo models; Bibliography; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":49083829322071,"sku":"9781108471138","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"planning-as-persuasive-storytelling-9780226799643","title":"Planning as Persuasive Storytelling","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis study looks at the world of political conflict surrounding the Commonwealth Edison Company's nuclear power plant construction programme in northern Illinois during the 1980s. It examines the theory that planning can best be thought of as a form of persuasive storytelling.","brand":"The University of Chicago Press","offers":[{"title":"Default Title","offer_id":49400122376535,"sku":"9780226799643","price":30.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780226799643.jpg?v=1730469798"},{"product_id":"how-to-think-about-information-9780252077555","title":"How to Think about Information","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThe history and theory of information as a commodity in the contemporary world \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\"\u003ci\u003eHow to Think About Information\u003c\/i\u003e is a critically important book. . . . Schiller provides fundamentally important insights into the infrastructural and superstructural demands of commodification.\"--\u003ci\u003eGlobal Media and Communication\u003c\/i\u003e\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\"Dan Schiller's oeuvre is clear and one that scholars must acknowledge if they deem themselves fit to reflect on the character of the information age.\"--\u003ci\u003eEuropean Journal of Comunication\u003c\/i\u003e\u003c\/p\u003e\u003cbr\u003e\"Dan Schiller is today probably the most lucid and critical scholar writing on the structure and history of communication and information systems--not just in the U.S., by the way--and this book demonstrates that in spades. He unites his usual clarity of vision of the present with his always-insightful examination and interpretation of communication history. This work will be another significant advancement of our knowledge, informing not just academic curiosity but also how we ought to think and rethink public policy that is shaping information and media today.\"--Richard Maxwell, professor of media studies, Queens College, City University of New York\u003cbr\u003e\"Read this book and you will never look at media convergence the same way again. By tracking business trends across media and telecom industries, Schiller demonstrates how much has been lost while citizens have been lulled by the discourses of globalization, deregulation, and the technology boom. Schiller's dazzling research and cogent argument make this book unforgettable.\"--Ellen Seiter, Stephen K. Nenno Professor of Television Studies, University of Southern California\u003cbr\u003e\"With a formidable command of knowledge in seemingly disparate fields and a truly transnational perspective, Dan Schiller cuts beneath the theoretical debates about information society and sifts through historical records and today's headlines to reveal the overarching logic of informationalized capitalism. The result is a profound, incisive and essential book for anybody interested in the contemporary world and the role of information in it.\"--Yuezhi Zhao, Canada Research Chair in Political Economy of Global Communication, Simon Fraser University","brand":"University of Illinois Press","offers":[{"title":"Default Title","offer_id":49400486691159,"sku":"9780252077555","price":19.94,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780252077555.jpg?v=1730470809"},{"product_id":"intercultural-communication-9780292755710","title":"Intercultural Communication","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAn authoritative, practical guide for deciphering and following the rules that govern cultures, with a demonstration of how these rules apply to the communication issues that exist between the United States and Mexico.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003e Preface \u003c\/li\u003e\n\u003cli\u003e Acknowledgments \u003c\/li\u003e\n\u003cli\u003e Part I. The Global Perspective of Intercultural Communication \u003cul\u003e\n\u003cli\u003e 1. Why Communicate across Cultures? \u003c\/li\u003e\n\u003cli\u003e 2. What Constitutes a Culture? \u003c\/li\u003e\n\u003cli\u003e 3. Obstacles of Perception \u003c\/li\u003e\n\u003cli\u003e 4. Obstacles in Verbal Processes \u003c\/li\u003e\n\u003cli\u003e 5. Obstacles in Nonverbal Processes \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e Part II. Two Worlds: The United States and Mexico \u003cul\u003e\n\u003cli\u003e 6. The Mexico-United States Cultural Environment \u003c\/li\u003e\n\u003cli\u003e 7. Some Mexico-United States Cultural Issues \u003c\/li\u003e\n\u003cli\u003e 8. Day-to-Day Cultural Interaction \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e Part III. Conclusion \u003cul\u003e\u003cli\u003e 9. Transcending Culture \u003c\/li\u003e\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e Appendix: Author's Note \u003c\/li\u003e\n\u003cli\u003e Glossary \u003c\/li\u003e\n\u003cli\u003e Notes \u003c\/li\u003e\n\u003cli\u003e Bibliography \u003c\/li\u003e\n\u003cli\u003e Index \u003c\/li\u003e\n\u003cli\u003e About the Author \u003c\/li\u003e\n\u003c\/ul\u003e","brand":"University of Texas Press","offers":[{"title":"Default Title","offer_id":49400876138839,"sku":"9780292755710","price":17.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780292755710.jpg?v=1730471815"},{"product_id":"introduction-to-cryptography-9780387207568","title":"Introduction to Cryptography","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e1 Integers.- 2 Congruences and Residue Class Rings.- 3 Encryption.- 4 Probability and Perfect Secrecy.- 5 DES.- 6 AES.- 7 Prime Number Generation.- 8 Public-Key Encryption.- 9 Factoring.- 10 Discrete Logarithms.- 11 Cryptographic Hash Functions.- 12 Digital Signatures.- 13 Other Systems.- 14 Identification.- 15 Secret Sharing.- 16 Public-Key Infrastructures.- Solutions of the exercises.- References.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eFrom the reviews:\u003c\/p\u003e \u003cp\u003eZentralblatt Math\u003c\/p\u003e \u003cp\u003e\"[......] Of the three books under review, Buchmann's is by far the most sophisticated, complete and up-to-date. It was written for computer-science majors -  German ones at that - and might be rough going for all but the best American undergraduates. It is amazing how much Buchmann is able to do in under 300 pages: self-contained explanations of the relevant mathematics (with proofs); a systematic introduction to symmetric cryptosystems, including a detailed description and discussion of DES; a good treatment of primality testing, integer factorization, and algorithms for discrete logarithms, clearly written sections describing most of the major types of cryptosystems, and explanations of basic concepts of practical cryptography such as hash functions, message authentication codes, signatures, passwords, certification authorities, and certificate chains. This book is an excellent reference, and I believe that it would also be a good textbook for a course for mathematics or computer science majors, provided that the instructor is prepared to supplement it with more leisurely treatments of some of the topics.\"\u003c\/p\u003e \u003cp\u003eN. Koblitz  (Seattle, WA)   - American Math. Society Monthly.\u003c\/p\u003e \u003cp\u003e\u003cem\u003eJ.A. Buchmann\u003c\/em\u003e\u003c\/p\u003e \u003cp\u003e\u003cem\u003eIntroduction to Cryptography\u003c\/em\u003e\u003c\/p\u003e \u003cp\u003e\u003cem\u003e\"It gives a clear and systematic introduction into the subject whose popularity is ever increasing, and can be recommended to all who would like to learn about cryptography. The book contains many exercises and examples. It can be used as a textbook and is likely to become popular among students. The necessary definitions and concepts from algebra, number theory and probability theory are formulated, illustrated by examples and applied to cryptography.\"\u003c\/em\u003e —ZENTRALBLATT MATH\u003c\/p\u003e \u003cp\u003e\"For those of use who wish to learn more about cryptography and\/or to teach it, Johannes Buchmann has written this book. … The book is mathematically complete and a satisfying read. There are plenty of homework exercises … . This is a good book for upperclassmen, graduate students, and faculty. … This book makes a superior reference and a fine textbook.\" (Robert W. Vallin, MathDL, January, 2001)\u003c\/p\u003e \u003cp\u003e\"Buchmann’s book is a text on cryptography intended to be used at the undergraduate level. … the intended audiences of this book are ‘readers who want to learn about modern cryptographic algorithms and their mathematical foundations … . I enjoy reading this book. … Readers will find a good exposition of the techniques used in developing and analyzing these algorithms. … These make Buchmann’s text an excellent choice for self study or as a text for students … in elementary number theory and algebra.\" (Andrew C. Lee, SIGACT News, Vol. 34 (4), 2003)\u003c\/p\u003e \u003cp\u003eFrom the reviews of the second edition:\u003c\/p\u003e \u003cp\u003e\"This is the english translation of the second edition of the author’s prominent german textbook ‘Einführung in die Kryptographie’. The original text grew out of several courses on cryptography given by the author at the Technical University Darmstadt; it is aimed at readers who want to learn about modern cryptographic techniques and its mathematical foundations … . As compared with the first edition the number of exercises has almost been doubled and some material … has been added.\" (R. Steinbauer, Monatshefte für Mathematik, Vol. 150 (4), 2007)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntegers.- Congruences and Residue Class Rings.- Encryption.- Probability and Perfect Secrecy.- DES.- AES.- Prime Number Generation.- Public-Key Encryption.- Factoring.- Discrete Logarithms.- Cryptographic Hash Functions.- Digital Signatures.- Other Systems.- Identification.- Public-Key Infrastructures.- Solutions of the Odd Exercises.- Subject Index.- Bibliography.","brand":"Springer-Verlag New York Inc.","offers":[{"title":"Default Title","offer_id":49401969082711,"sku":"9780387207568","price":56.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780387207568.jpg?v=1730479002"},{"product_id":"systems-practice-in-the-information-society-9780415992305","title":"Systems Practice in the Information Society","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eAs a collection of ideas and methodologies, systems thinking has made an impact in organizations and in particular in the information systems field. However, this main emphasis on organizations limits the scope of systems thinking and practice. There is a need first to use systems thinking in addressing societal problems, and second to enable people involved in developing the information society to reflect on the impacts of systems and technologies in society as a whole. Thus, there are opportunities to review the scope and potential of systems thinking and practice to deal with information society-related issues. \u003c\/p\u003e\u003cp\u003e\u003cem\u003e \u003cbr\u003e\u003cp\u003eSystems Practice in the Information Society\u003c\/p\u003e\u003c\/em\u003e provides students of information systems as well as practicing Inofrmation Systems managers with concepts and strategies to enable them to understand and use systems thinking methodologies and address challenges posed by the development of information-based societies. This book brings experiences, idea\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e1. Introduction\u003c\/p\u003e\u003cp\u003e 2. The Information Society \u003c\/p\u003e\u003cp\u003e3. Systems-Thinking \u003c\/p\u003e\u003cp\u003e4. Applied Systems-Thinking \u003c\/p\u003e\u003cp\u003e5. Idealist Pattern for Practice in the Information Society \u003c\/p\u003e\u003cp\u003e6. Strategic Pattern of Pracitice for the Information Society \u003c\/p\u003e\u003cp\u003e7. Power-based Pattern for Practice in the Information Society \u003c\/p\u003e\u003cp\u003e8. A Dynamic Practice Framework for Living and Working in the Information Society \u003c\/p\u003e\u003cp\u003e9. Conclusions\u003c\/p\u003e","brand":"Taylor \u0026 Francis Ltd","offers":[{"title":"Default Title","offer_id":49402163659095,"sku":"9780415992305","price":171.0,"currency_code":"GBP","in_stock":true}]},{"product_id":"distributed-source-coding-9780470688991","title":"Distributed Source Coding","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eDistributed source coding is one of the key enablers for efficient cooperative communication. The potential applications range from wireless sensor networks, ad-hoc networks, and surveillance networks, to robust low-complexity video coding, stereo\/Multiview video coding, HDTV, hyper-spectral and multispectral imaging, and biometrics.\u003c\/p\u003e \u003cp\u003eThe book is divided into three sections: theory, algorithms, and applications. Part one covers the background of information theory with an emphasis on DSC; part two discusses designs of algorithmic solutions for DSC problems, covering the three most important DSC problems: Slepian-Wolf, Wyner-Ziv, and MT source coding; and part three is dedicated to a variety of potential DSC applications.\u003c\/p\u003e \u003cp\u003eKey features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eClear explanation of distributed source coding theory and algorithms including both lossless and lossy designs.\u003c\/li\u003e \u003cli\u003eRich applications of distributed source coding, which covers multimedia communication and data security a\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eAcknowledgment xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAbout the Companion Website xvii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.1 What is Distributed Source Coding? 2\u003c\/p\u003e \u003cp\u003e1.2 Historical Overview and Background 2\u003c\/p\u003e \u003cp\u003e1.3 Potential and Applications 3\u003c\/p\u003e \u003cp\u003e1.4 Outline 4\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Theory of Distributed Source Coding 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Lossless Compression of Correlated Sources 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Slepian–Wolf Coding 10\u003c\/p\u003e \u003cp\u003e2.1.1 Proof of the SWTheorem 15\u003c\/p\u003e \u003cp\u003eAchievability of the SWTheorem 16\u003c\/p\u003e \u003cp\u003eConverse of the SWTheorem 19\u003c\/p\u003e \u003cp\u003e2.2 Asymmetric and Symmetric SWCoding 21\u003c\/p\u003e \u003cp\u003e2.3 SWCoding of Multiple Sources 22\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Wyner–Ziv Coding Theory 25\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Forward Proof ofWZ Coding 27\u003c\/p\u003e \u003cp\u003e3.2 Converse Proof of WZ Coding 29\u003c\/p\u003e \u003cp\u003e3.3 Examples 30\u003c\/p\u003e \u003cp\u003e3.3.1 Doubly Symmetric Binary Source 30\u003c\/p\u003e \u003cp\u003eProblem Setup 30\u003c\/p\u003e \u003cp\u003eA Proposed Scheme 31\u003c\/p\u003e \u003cp\u003eVerify the Optimality of the Proposed Scheme 32\u003c\/p\u003e \u003cp\u003e3.3.2 Quadratic Gaussian Source 35\u003c\/p\u003e \u003cp\u003eProblem Setup 35\u003c\/p\u003e \u003cp\u003eProposed Scheme 36\u003c\/p\u003e \u003cp\u003eVerify the Optimality of the Proposed Scheme 37\u003c\/p\u003e \u003cp\u003e3.4 Rate Loss of theWZ Problem 38\u003c\/p\u003e \u003cp\u003eBinary Source Case 39\u003c\/p\u003e \u003cp\u003eRate loss of General Cases 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Lossy Distributed Source Coding 41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Berger–Tung Inner Bound 42\u003c\/p\u003e \u003cp\u003e4.1.1 Berger–Tung Scheme 42\u003c\/p\u003e \u003cp\u003eCodebook Preparation 42\u003c\/p\u003e \u003cp\u003eEncoding 42\u003c\/p\u003e \u003cp\u003eDecoding 43\u003c\/p\u003e \u003cp\u003e4.1.2 Distortion Analysis 43\u003c\/p\u003e \u003cp\u003e4.2 Indirect Multiterminal Source Coding 45\u003c\/p\u003e \u003cp\u003e4.2.1 Quadratic Gaussian CEO Problem with Two Encoders 45\u003c\/p\u003e \u003cp\u003eForward Proof of Quadratic Gaussian CEO Problem with Two Terminals 46\u003c\/p\u003e \u003cp\u003eConverse Proof of Quadratic Gaussian CEO Problem with Two Terminals 48\u003c\/p\u003e \u003cp\u003e4.3 Direct Multiterminal Source Coding 54\u003c\/p\u003e \u003cp\u003e4.3.1 Forward Proof of Gaussian Multiterminal Source Coding Problem with Two Sources 55\u003c\/p\u003e \u003cp\u003e4.3.2 Converse Proof of Gaussian Multiterminal Source Coding Problem with Two Sources 63\u003c\/p\u003e \u003cp\u003eBounds for R1 and R2 64\u003c\/p\u003e \u003cp\u003eCollaborative Lower Bound 66\u003c\/p\u003e \u003cp\u003e𝜇-sum Bound 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Implementation 75\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Slepian–Wolf Code Designs Based on Channel Coding 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Asymmetric SWCoding 77\u003c\/p\u003e \u003cp\u003e5.1.1 Binning Idea 78\u003c\/p\u003e \u003cp\u003e5.1.2 Syndrome-based Approach 79\u003c\/p\u003e \u003cp\u003eHamming Binning 80\u003c\/p\u003e \u003cp\u003eSWEncoding 80\u003c\/p\u003e \u003cp\u003eSWDecoding 80\u003c\/p\u003e \u003cp\u003eLDPC-based SWCoding 81\u003c\/p\u003e \u003cp\u003e5.1.3 Parity-based Approach 82\u003c\/p\u003e \u003cp\u003e5.1.4 Syndrome-based Versus Parity-based Approach 84\u003c\/p\u003e \u003cp\u003e5.2 Non-asymmetric SWCoding 85\u003c\/p\u003e \u003cp\u003e5.2.1 Generalized Syndrome-based Approach 86\u003c\/p\u003e \u003cp\u003e5.2.2 Implementation using IRA Codes 88\u003c\/p\u003e \u003cp\u003e5.3 Adaptive Slepian–Wolf Coding 90\u003c\/p\u003e \u003cp\u003e5.3.1 Particle-based Belief Propagation for SWCoding 91\u003c\/p\u003e \u003cp\u003e5.4 Latest Developments and Trends 93\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Distributed Arithmetic Coding 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Arithmetic Coding 97\u003c\/p\u003e \u003cp\u003e6.2 Distributed Arithmetic Coding 101\u003c\/p\u003e \u003cp\u003e6.3 Definition of the DAC Spectrum 103\u003c\/p\u003e \u003cp\u003e6.3.1 Motivations 103\u003c\/p\u003e \u003cp\u003e6.3.2 Initial DAC Spectrum 104\u003c\/p\u003e \u003cp\u003e6.3.3 Depth-i DAC Spectrum 105\u003c\/p\u003e \u003cp\u003e6.3.4 Some Simple Properties of the DAC Spectrum 107\u003c\/p\u003e \u003cp\u003e6.4 Formulation of the Initial DAC Spectrum 107\u003c\/p\u003e \u003cp\u003e6.5 Explicit Form of the Initial DAC Spectrum 110\u003c\/p\u003e \u003cp\u003e6.6 Evolution of the DAC Spectrum 113\u003c\/p\u003e \u003cp\u003e6.7 Numerical Calculation of the DAC Spectrum 116\u003c\/p\u003e \u003cp\u003e6.7.1 Numerical Calculation of the Initial DAC Spectrum 117\u003c\/p\u003e \u003cp\u003e6.7.2 Numerical Estimation of DAC Spectrum Evolution 118\u003c\/p\u003e \u003cp\u003e6.8 Analyses on DAC Codes with Spectrum 120\u003c\/p\u003e \u003cp\u003e6.8.1 Definition of DAC Codes 121\u003c\/p\u003e \u003cp\u003e6.8.2 Codebook Cardinality 122\u003c\/p\u003e \u003cp\u003e6.8.3 Codebook Index Distribution 123\u003c\/p\u003e \u003cp\u003e6.8.4 Rate Loss 123\u003c\/p\u003e \u003cp\u003e6.8.5 Decoder Complexity 124\u003c\/p\u003e \u003cp\u003e6.8.6 Decoding Error Probability 126\u003c\/p\u003e \u003cp\u003e6.9 Improved Binary DAC Codec 130\u003c\/p\u003e \u003cp\u003e6.9.1 Permutated BDAC Codec 130\u003c\/p\u003e \u003cp\u003ePrinciple 130\u003c\/p\u003e \u003cp\u003eProof of SWLimit Achievability 131\u003c\/p\u003e \u003cp\u003e6.9.2 BDAC Decoder withWeighted Branching 132\u003c\/p\u003e \u003cp\u003e6.10 Implementation of the Improved BDAC Codec 134\u003c\/p\u003e \u003cp\u003e6.10.1 Encoder 134\u003c\/p\u003e \u003cp\u003ePrinciple 134\u003c\/p\u003e \u003cp\u003eImplementation 135\u003c\/p\u003e \u003cp\u003e6.10.2 Decoder 135\u003c\/p\u003e \u003cp\u003ePrinciple 135\u003c\/p\u003e \u003cp\u003eImplementation 136\u003c\/p\u003e \u003cp\u003e6.11 Experimental Results 138\u003c\/p\u003e \u003cp\u003eEffect of Segment Size on Permutation Technique 139\u003c\/p\u003e \u003cp\u003eEffect of Surviving-Path Number onWB Technique 139\u003c\/p\u003e \u003cp\u003eComparison with LDPC Codes 139\u003c\/p\u003e \u003cp\u003eApplication of PBDAC to Nonuniform Sources 140\u003c\/p\u003e \u003cp\u003e6.12 Conclusion 141\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Wyner–Ziv Code Design 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Vector Quantization 143\u003c\/p\u003e \u003cp\u003e7.2 Lattice Theory 146\u003c\/p\u003e \u003cp\u003e7.2.1 What is a Lattice? 146\u003c\/p\u003e \u003cp\u003eExamples 146\u003c\/p\u003e \u003cp\u003eDual Lattice 147\u003c\/p\u003e \u003cp\u003eIntegral Lattice 147\u003c\/p\u003e \u003cp\u003eLattice Quantization 148\u003c\/p\u003e \u003cp\u003e7.2.2 What is a Good Lattice? 149\u003c\/p\u003e \u003cp\u003ePacking Efficiency 149\u003c\/p\u003e \u003cp\u003eCovering Efficiency 150\u003c\/p\u003e \u003cp\u003eNormalized Second Moment 150\u003c\/p\u003e \u003cp\u003eKissing Number 150\u003c\/p\u003e \u003cp\u003eSome Good Lattices 151\u003c\/p\u003e \u003cp\u003e7.3 Nested Lattice Quantization 151\u003c\/p\u003e \u003cp\u003eEncoding\/decoding 152\u003c\/p\u003e \u003cp\u003eCoset Binning 152\u003c\/p\u003e \u003cp\u003eQuantization Loss and Binning Loss 153\u003c\/p\u003e \u003cp\u003eSW Coded NLQ 154\u003c\/p\u003e \u003cp\u003e7.3.1 Trellis Coded Quantization 154\u003c\/p\u003e \u003cp\u003e7.3.2 Principle of TCQ 155\u003c\/p\u003e \u003cp\u003eGeneration of Codebooks 156\u003c\/p\u003e \u003cp\u003eGeneration of Trellis from Convolutional Codes 156\u003c\/p\u003e \u003cp\u003eMapping of Trellis Branches onto Sub-codebooks 157\u003c\/p\u003e \u003cp\u003eQuantization 157\u003c\/p\u003e \u003cp\u003eExample 158\u003c\/p\u003e \u003cp\u003e7.4 WZ Coding Based on TCQ and LDPC Codes 159\u003c\/p\u003e \u003cp\u003e7.4.1 Statistics of TCQ Indices 159\u003c\/p\u003e \u003cp\u003e7.4.2 LLR of Trellis Bits 162\u003c\/p\u003e \u003cp\u003e7.4.3 LLR of Codeword Bits 163\u003c\/p\u003e \u003cp\u003e7.4.4 Minimum MSE Estimation 163\u003c\/p\u003e \u003cp\u003e7.4.5 Rate Allocation of Bit-planes 164\u003c\/p\u003e \u003cp\u003e7.4.6 Experimental Results 166\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Applications 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Wyner–Ziv Video Coding 169\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Basic Principle 169\u003c\/p\u003e \u003cp\u003e8.2 Benefits of WZ Video Coding 170\u003c\/p\u003e \u003cp\u003e8.3 Key Components of WZ Video Decoding 171\u003c\/p\u003e \u003cp\u003e8.3.1 Side-information Preparation 171\u003c\/p\u003e \u003cp\u003eBidirectional Motion Compensation 172\u003c\/p\u003e \u003cp\u003e8.3.2 Correlation Modeling 173\u003c\/p\u003e \u003cp\u003eExploiting Spatial Redundancy 174\u003c\/p\u003e \u003cp\u003e8.3.3 Rate Controller 175\u003c\/p\u003e \u003cp\u003e8.4 Other Notable Features of Miscellaneous WZ Video Coders 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Correlation Estimation in DVC 177\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Background to Correlation Parameter Estimation in DVC 177\u003c\/p\u003e \u003cp\u003e9.1.1 Correlation Model inWZ Video Coding 177\u003c\/p\u003e \u003cp\u003e9.1.2 Offline Correlation Estimation 178\u003c\/p\u003e \u003cp\u003ePixel Domain Offline Correlation Estimation 178\u003c\/p\u003e \u003cp\u003eTransform Domain Offline Correlation Estimation 180\u003c\/p\u003e \u003cp\u003e9.1.3 Online Correlation Estimation 181\u003c\/p\u003e \u003cp\u003ePixel Domain Online Correlation Estimation 182\u003c\/p\u003e \u003cp\u003eTransform Domain Online Correlation Estimation 184\u003c\/p\u003e \u003cp\u003e9.2 Recap of Belief Propagation and Particle Filter Algorithms 185\u003c\/p\u003e \u003cp\u003e9.2.1 Belief Propagation Algorithm 185\u003c\/p\u003e \u003cp\u003e9.2.2 Particle Filtering 186\u003c\/p\u003e \u003cp\u003e9.3 Correlation Estimation in DVC with Particle Filtering 187\u003c\/p\u003e \u003cp\u003e9.3.1 Factor Graph Construction 187\u003c\/p\u003e \u003cp\u003e9.3.2 Correlation Estimation in DVC with Particle Filtering 190\u003c\/p\u003e \u003cp\u003e9.3.3 Experimental Results 192\u003c\/p\u003e \u003cp\u003e9.3.4 Conclusion 197\u003c\/p\u003e \u003cp\u003e9.4 Low Complexity Correlation Estimation using Expectation Propagation 199\u003c\/p\u003e \u003cp\u003e9.4.1 System Architecture 199\u003c\/p\u003e \u003cp\u003e9.4.2 Factor Graph Construction 199\u003c\/p\u003e \u003cp\u003eJoint Bit-plane SWCoding (Region II) 200\u003c\/p\u003e \u003cp\u003eCorrelation Parameter Tracking (Region I) 201\u003c\/p\u003e \u003cp\u003e9.4.3 Message Passing on the Constructed Factor Graph 202\u003c\/p\u003e \u003cp\u003eExpectation Propagation 203\u003c\/p\u003e \u003cp\u003e9.4.4 Posterior Approximation of the Correlation Parameter using Expectation Propagation 204\u003c\/p\u003e \u003cp\u003eMoment Matching 205\u003c\/p\u003e \u003cp\u003e9.4.5 Experimental Results 206\u003c\/p\u003e \u003cp\u003e9.4.6 Conclusion 211\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 DSC for Solar Image Compression 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Background 213\u003c\/p\u003e \u003cp\u003e10.2 RelatedWork 215\u003c\/p\u003e \u003cp\u003e10.3 Distributed Multi-view Image Coding 217\u003c\/p\u003e \u003cp\u003e10.4 Adaptive Joint Bit-plane WZ Decoding of Multi-view Images with Disparity Estimation 217\u003c\/p\u003e \u003cp\u003e10.4.1 Joint Bit-planeWZ Decoding 217\u003c\/p\u003e \u003cp\u003e10.4.2 Joint Bit-planeWZ Decoding with Disparity Estimation 219\u003c\/p\u003e \u003cp\u003e10.4.3 Joint Bit-planeWZ Decoding with Correlation Estimation 220\u003c\/p\u003e \u003cp\u003e10.5 Results and Discussion 221\u003c\/p\u003e \u003cp\u003e10.6 Summary 224\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Secure Distributed Image Coding 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Background 225\u003c\/p\u003e \u003cp\u003e11.2 System Architecture 227\u003c\/p\u003e \u003cp\u003e11.2.1 Compression of Encrypted Data 228\u003c\/p\u003e \u003cp\u003e11.2.2 Joint Decompression and Decryption Design 230\u003c\/p\u003e \u003cp\u003e11.3 Practical Implementation Issues 233\u003c\/p\u003e \u003cp\u003e11.4 Experimental Results 233\u003c\/p\u003e \u003cp\u003e11.4.1 Experiment Setup 234\u003c\/p\u003e \u003cp\u003e11.4.2 Security and Privacy Protection 235\u003c\/p\u003e \u003cp\u003e11.4.3 Compression Performance 236\u003c\/p\u003e \u003cp\u003e11.5 Discussion 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Secure Biometric Authentication Using DSC 241\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Background 241\u003c\/p\u003e \u003cp\u003e12.2 RelatedWork 243\u003c\/p\u003e \u003cp\u003e12.3 System Architecture 245\u003c\/p\u003e \u003cp\u003e12.3.1 Feature Extraction 246\u003c\/p\u003e \u003cp\u003e12.3.2 Feature Pre-encryption 248\u003c\/p\u003e \u003cp\u003e12.3.3 SeDSC Encrypter\/decrypter 248\u003c\/p\u003e \u003cp\u003e12.3.4 Privacy-preserving Authentication 249\u003c\/p\u003e \u003cp\u003e12.4 SeDSC Encrypter Design 249\u003c\/p\u003e \u003cp\u003e12.4.1 Non-asymmetric SWCodes with Code Partitioning 250\u003c\/p\u003e \u003cp\u003e12.4.2 Implementation of SeDSC Encrypter using IRA Codes 251\u003c\/p\u003e \u003cp\u003e12.5 SeDSC Decrypter Design 252\u003c\/p\u003e \u003cp\u003e12.6 Experiments 256\u003c\/p\u003e \u003cp\u003e12.6.1 Dataset and Experimental Setup 256\u003c\/p\u003e \u003cp\u003e12.6.2 Feature Length Selection 257\u003c\/p\u003e \u003cp\u003e12.6.3 Authentication Accuracy 257\u003c\/p\u003e \u003cp\u003eAuthentication Performances on Small Feature Length (i.e., \u003ci\u003eN\u003c\/i\u003e = 100) 257\u003c\/p\u003e \u003cp\u003ePerformances on Large Feature Lengths (i.e., \u003ci\u003eN\u003c\/i\u003e ≥ 300) 258\u003c\/p\u003e \u003cp\u003e12.6.4 Privacy and Security 259\u003c\/p\u003e \u003cp\u003e12.6.5 Complexity Analysis 261\u003c\/p\u003e \u003cp\u003e12.7 Discussion 261\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Basic Information Theory 263\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Information Measures 263\u003c\/p\u003e \u003cp\u003eA.1.1 Entropy 263\u003c\/p\u003e \u003cp\u003eA.1.2 Relative Entropy 267\u003c\/p\u003e \u003cp\u003eA.1.3 Mutual Information 268\u003c\/p\u003e \u003cp\u003eA.1.4 Entropy Rate 269\u003c\/p\u003e \u003cp\u003eA.2 Independence and Mutual Information 270\u003c\/p\u003e \u003cp\u003eA.3 Venn Diagram Interpretation 273\u003c\/p\u003e \u003cp\u003eA.4 Convexity and Jensen’s Inequality 274\u003c\/p\u003e \u003cp\u003eA.5 Differential Entropy 277\u003c\/p\u003e \u003cp\u003eA.5.1 Gaussian Random Variables 278\u003c\/p\u003e \u003cp\u003eA.5.2 Entropy Power Inequality 278\u003c\/p\u003e \u003cp\u003eA.6 Typicality 279\u003c\/p\u003e \u003cp\u003eA.6.1 Jointly Typical Sequences 282\u003c\/p\u003e \u003cp\u003eA.7 Packing Lemmas and Covering Lemmas 284\u003c\/p\u003e \u003cp\u003eA.8 Shannon’s Source CodingTheorem 286\u003c\/p\u003e \u003cp\u003eA.9 Lossy Source Coding—Rate-distortionTheorem 289\u003c\/p\u003e \u003cp\u003eA.9.1 Rate-distortion Problem with Side Information 291\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB Background on Channel Coding 293\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Linear Block Codes 294\u003c\/p\u003e \u003cp\u003eB.1.1 Syndrome Decoding of Block Codes 295\u003c\/p\u003e \u003cp\u003eB.1.2 Hamming Codes, Packing Bound, and Perfect Codes 295\u003c\/p\u003e \u003cp\u003eB.2 Convolutional Codes 297\u003c\/p\u003e \u003cp\u003eB.2.1 Viterbi Decoding Algorithm 298\u003c\/p\u003e \u003cp\u003eB.3 Shannon’s Channel CodingTheorem 301\u003c\/p\u003e \u003cp\u003eB.3.1 Achievability Proof of the Channel CodingTheorem 303\u003c\/p\u003e \u003cp\u003eB.3.2 Converse Proof of Channel CodingTheorem 305\u003c\/p\u003e \u003cp\u003eB.4 Low-density Parity-check Codes 306\u003c\/p\u003e \u003cp\u003eB.4.1 A Quick Summary of LDPC Codes 306\u003c\/p\u003e \u003cp\u003eB.4.2 Belief Propagation Algorithm 307\u003c\/p\u003e \u003cp\u003eB.4.3 LDPC Decoding using BP 312\u003c\/p\u003e \u003cp\u003eB.4.4 IRA Codes 314\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC Approximate Inference 319\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eC.1 Stochastic Approximation 319\u003c\/p\u003e \u003cp\u003eC.1.1 Importance SamplingMethods 320\u003c\/p\u003e \u003cp\u003eC.1.2 Markov Chain Monte Carlo 321\u003c\/p\u003e \u003cp\u003eMarkov Chains 321\u003c\/p\u003e \u003cp\u003eMarkov Chain Monte Carlo 321\u003c\/p\u003e \u003cp\u003eC.2 Deterministic Approximation 322\u003c\/p\u003e \u003cp\u003eC.2.1 Preliminaries 322\u003c\/p\u003e \u003cp\u003eExponential Family 322\u003c\/p\u003e \u003cp\u003eKullback–Leibler Divergence 323\u003c\/p\u003e \u003cp\u003eAssumed-density Filtering 324\u003c\/p\u003e \u003cp\u003eC.2.2 Expectation Propagation 325\u003c\/p\u003e \u003cp\u003eRelationship with BP 326\u003c\/p\u003e \u003cp\u003eC.2.3 Relationship with Other Variational Inference Methods 328\u003c\/p\u003e \u003cp\u003e\u003cb\u003eD Multivariate Gaussian Distribution 331\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eD.1 Introduction 331\u003c\/p\u003e \u003cp\u003eD.2 Probability Density Function 331\u003c\/p\u003e \u003cp\u003eD.3 Marginalization 332\u003c\/p\u003e \u003cp\u003eD.4 Conditioning 333\u003c\/p\u003e \u003cp\u003eD.5 Product of Gaussian pdfs 334\u003c\/p\u003e \u003cp\u003eD.6 Division of Gaussian pdfs 337\u003c\/p\u003e \u003cp\u003eD.7 Mixture of Gaussians 337\u003c\/p\u003e \u003cp\u003eD.7.1 Reduce the Number of Components in Gaussian Mixtures 338\u003c\/p\u003e \u003cp\u003eWhich Components to Merge? 340\u003c\/p\u003e \u003cp\u003eHow to Merge Components? 341\u003c\/p\u003e \u003cp\u003eD.8 Summary 342\u003c\/p\u003e \u003cp\u003eAppendix: Matrix Equations 343\u003c\/p\u003e \u003cp\u003eBibliography 345\u003c\/p\u003e \u003cp\u003eIndex 357\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402412171607,"sku":"9780470688991","price":89.95,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470688991.jpg?v=1730480320"},{"product_id":"elements-of-information-theory-wiley-series-in-telecommunications-and-signal-processing-9780471241959","title":"Elements of Information Theory Wiley Series in","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe latest edition of this classic is updated with new problem sets and material\u003cbr\u003e \u003cbr\u003e \u003cbr\u003e 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.\u003cbr\u003e \u003cbr\u003e 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. The historical notes that follow each chapter recap the main points.\u003cbr\u003e \u003cbr\u003e The Second Edition features:\u003cbr\u003e * Chapters reorganized to improve teaching\u003cbr\u003e * 200 new problems\u003cbr\u003e * New material on source coding, portfolio theory, and feedback capacity\u003cbr\u003e * Updated referenc\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\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)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\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","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402534232407,"sku":"9780471241959","price":92.66,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471241959.jpg?v=1730480699"},{"product_id":"nexus-9780593734223","title":"Nexus","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Random House Publishing Group","offers":[{"title":"Default Title","offer_id":49403318927703,"sku":"9780593734223","price":22.91,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780593734223.jpg?v=1730483115"},{"product_id":"imaginary-futures-from-thinking-machines-to-the-global-village-9780745326610","title":"Imaginary Futures From Thinking Machines to the","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book is a history of the future. It shows how our contemporary understanding of the Net is shaped by visions of the future that were put together in the 1950s and 1960s.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Barbrook has an amusing take on our distorted - if not delusional - relationship with technology, but his underlying point is serious: future visions of technology are used to distract us and also control us, and if we forget these imaginary futures, we are likely to repeat them' -- Guardian Unlimited\u003cbr\u003e'A compelling, authoritative, and painstakingly documented narrative, Imaginary Futures traces the emergence of the computer era in the context of desperately competing ideologies, economics, and empires. This is a work of passionate and persuasive scholarship by a contemporary social theorist at the top of his game' -- Douglas Rushkoff, author, Coercion, Media Virus, Get Back in the Box.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. The Future Is What It Used To Be\u003cbr\u003e  2. The American Century\t\u003cbr\u003e  3. Cold War Computing\u003cbr\u003e  4. The Human Machine\u003cbr\u003e  5. Cybernetic Supremacy\t\u003cbr\u003e  6. The Global Village\u003cbr\u003e  7. The Cold War Left\t\u003cbr\u003e  8. The Chosen Few\t\u003cbr\u003e  9. Free Workers In The Affluent Society\t\t\u003cbr\u003e  10. The Prophets Of Post-Industrialism\t\u003cbr\u003e  11. The American Road to Cybernetic Communism\t\t\u003cbr\u003e  12. The Leader Of The Free World\t\t\u003cbr\u003e  13. The Great Game\t\t\t\t\u003cbr\u003e  14. The American Invasion Of Vietnam\t\t\t\t\u003cbr\u003e  15. Those Who Forget The Future Are Condemned To Repeat It \u003cbr\u003e  References\u003cbr\u003e  Index","brand":"Pluto Press","offers":[{"title":"Default Title","offer_id":49404294496599,"sku":"9780745326610","price":72.25,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780745326610.jpg?v=1730486009"},{"product_id":"furious-technological-feminism-and-digital-futures-digital-barricades-9780745340494","title":"Furious Technological Feminism and Digital","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA major work of feminist critical theory challenging the masculinist politics of digital media forms, practices and study.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Furious rips beyond the vanity of know-it-all analysis to offer long-awaited new ways of thinking, feeling, and writing. Cunningly crafted by an authorial trio, it bewitches with performative feminist energies. I dare you to read it' -- Sally-Jane Norman, Founding Director of the Attenborough Centre for the Creative Arts at the University of Sussex\u003cbr\u003e'A rare gem of a book, Furious makes a sharp, critical feminist intervention in digital media research demonstrating the power of thinking together, going against the conventions of academic writing, and creating good trouble' -- Susanna Paasonen, co-author of 'NSFW: Sex, Humor, and Risk in Social Media'\u003cbr\u003e'This wide-ranging and imaginative book makes a compelling case for a feminist techno-politics which challenges to the core the masculinist grip of computational culture and science. It's also a book which pays fine attention to the process of writing' -- Angela McRobbie, author of 'Be Creative: Making a Living in the New Culture Industries'\u003cbr\u003e'A passionate guidebook to feminist theorising that refuses data as self-evident patterns and theory as beautiful abstractions, while insisting on the generative power of writing, fabulation, and future making' -- Lucy Suchman, author of 'Feminist STS and the Sciences of the Artificial'\u003cbr\u003e'Combining clarity with rational ire, Furious insists on the power of insurgent, intersectional feminist epistemologies to disrupt, inspire, and transform. This collective feminist tour de force writes us a new course, away from the technophilic belief that technology will fix what ails the contemporary world and toward a critical and temperate utopianism' -- Carol Stabile, author of 'The Broadcast 41: Women and the Anti-Communist Blacklist'\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eSeries Preface\u003cbr\u003e  Acknowledgements\u003cbr\u003e  Preface\u003cbr\u003e  1. Feminist Futures: A Conditional Paeon for the Anything-Digital\u003cbr\u003e  2. Scale, Subject and Stories: Unreal Objects\u003cbr\u003e  3. Bland Ambition? Automation's Missing Visions\u003cbr\u003e  4. Driving at the Anthropocene, or, Let's Get Out of Here: How?\u003cbr\u003e  5. Technological Feminism and Digital Futures\u003cbr\u003e  Bibliography\u003cbr\u003e  Index","brand":"Pluto Press","offers":[{"title":"Default Title","offer_id":49404320186711,"sku":"9780745340494","price":72.25,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780745340494.jpg?v=1730486090"},{"product_id":"furious-9780745340500","title":"Furious","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA major work of feminist critical theory challenging the masculinist politics of digital media forms, practices and study.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Furious rips beyond the vanity of know-it-all analysis to offer long-awaited new ways of thinking, feeling, and writing. Cunningly crafted by an authorial trio, it bewitches with performative feminist energies. I dare you to read it' -- Sally-Jane Norman, Founding Director of the Attenborough Centre for the Creative Arts at the University of Sussex\u003cbr\u003e'A rare gem of a book, Furious makes a sharp, critical feminist intervention in digital media research demonstrating the power of thinking together, going against the conventions of academic writing, and creating good trouble' -- Susanna Paasonen, co-author of 'NSFW: Sex, Humor, and Risk in Social Media'\u003cbr\u003e'This wide-ranging and imaginative book makes a compelling case for a feminist techno-politics which challenges to the core the masculinist grip of computational culture and science. It's also a book which pays fine attention to the process of writing' -- Angela McRobbie, author of 'Be Creative: Making a Living in the New Culture Industries'\u003cbr\u003e'A passionate guidebook to feminist theorising that refuses data as self-evident patterns and theory as beautiful abstractions, while insisting on the generative power of writing, fabulation, and future making' -- Lucy Suchman, author of 'Feminist STS and the Sciences of the Artificial'\u003cbr\u003e'Combining clarity with rational ire, Furious insists on the power of insurgent, intersectional feminist epistemologies to disrupt, inspire, and transform. This collective feminist tour de force writes us a new course, away from the technophilic belief that technology will fix what ails the contemporary world and toward a critical and temperate utopianism' -- Carol Stabile, author of 'The Broadcast 41: Women and the Anti-Communist Blacklist'\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eSeries Preface\u003cbr\u003e  Acknowledgements\u003cbr\u003e  Preface\u003cbr\u003e  1. Feminist Futures: A Conditional Paeon for the Anything-Digital\u003cbr\u003e  2. Scale, Subject and Stories: Unreal Objects\u003cbr\u003e  3. Bland Ambition? Automation's Missing Visions\u003cbr\u003e  4. Driving at the Anthropocene, or, Let's Get Out of Here: How?\u003cbr\u003e  5. Technological Feminism and Digital Futures\u003cbr\u003e  Bibliography\u003cbr\u003e  Index","brand":"Pluto Press","offers":[{"title":"Default Title","offer_id":49404320219479,"sku":"9780745340500","price":20.69,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780745340500.jpg?v=1730486091"},{"product_id":"computing-as-writing-9780816697021","title":"Computing as Writing","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eWhat does it mean to be a writer today? Is writing code for an app equivalent to writing a novel? Should we change how we teach writing? \u003ci\u003eComputing as Writing\u003c\/i\u003e ponders both the implications and contradictions of the common metaphor that equates computing and writing, from \"notebook\" computers to \"writing\" code.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\"Daniel Punday traces the idea—an idea that he shows to be pervasive—that to control computers we typically engage in a sort of writing. This insight informs our understanding of computation in culture and also enriches our notion of writing generally. It should, additionally, help non-programmer humanists see that, since they have learned to write, they can learn to do that specific type of writing that is known as programming.\"—Nick Montfort, Massachusetts Institute of Technology\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\"In a world in which the distinction between writing and computing is increasingly blurred, Punday's volume raises some intriguing questions and offers some new ways to look at writing and computing.\"—\u003ci\u003eCHOICE\u003c\/i\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eContents\u003c\/p\u003e\u003cp\u003ePreface\u003cbr\u003e1. My Documents: Remembering the Memex\u003cbr\u003e2. Writing, Work, and Profession\u003cbr\u003e3. Programmer as Writer\u003cbr\u003e4. E-books, Libraries, and Feelies\u003cbr\u003e5. Invention, Patents, and the Technological System\u003cbr\u003e6. Audience Today: Between Literature and Performance\u003cbr\u003eConclusion: Invention, Creativity, and the Teaching of Writing\u003cbr\u003eAcknowledgments\u003cbr\u003eNotes\u003cbr\u003eIndex\u003cbr\u003e\u003c\/p\u003e","brand":"University of Minnesota Press","offers":[{"title":"Default Title","offer_id":49405977657687,"sku":"9780816697021","price":19.94,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780816697021.jpg?v=1730494117"},{"product_id":"effective-machine-learning-teams-9781098144630","title":"Effective Machine Learning Teams","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":49406793908567,"sku":"9781098144630","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098144630.jpg?v=1730497130"},{"product_id":"algebraic-curves-in-cryptography-9781420079463","title":"Algebraic Curves in Cryptography","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThe reach of algebraic curves in cryptography goes far beyond elliptic curve or public key cryptography yet these other application areas have not been systematically covered in the literature. Addressing this gap, \u003cstrong\u003eAlgebraic Curves in Cryptography\u003c\/strong\u003e explores the rich uses of algebraic curves in a range of cryptographic applications, such as secret sharing, frameproof codes, and broadcast encryption. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eSuitable for researchers and graduate students in mathematics and computer science, this self-contained book is one of the first to focus on many topics in cryptography involving algebraic curves. After supplying the necessary background on algebraic curves, the authors discuss error-correcting codes, including algebraic geometry codes, and provide an introduction to elliptic curves. Each chapter in the remainder of the book deals with a selected topic in cryptography (other than elliptic curve cryptography). The topics covered include secret sharing schemes,\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\"This is a self-contained book intended for researchers and graduate students in mathematics and computer science interested in different topics in cryptography involving algebraic curves ... The authors of this book make an exhaustive review on some other topics where algebraic curves, mainly in higher genus, are important as well.\"\u003cbr\u003e—\u003cem\u003eZentralblatt MATH\u003c\/em\u003e 1282\u003c\/p\u003e\u003cp\u003e\"The book is written in a user-friendly style, with good coverage of the background, many examples, and a detailed bibliography of over 180 items. It is mainly directed towards graduate students and researchers, but some parts of the book are even accessible for advanced undergraduate students. The book is highly recommended for readers interested in the manifold applications of algebraic curves over finite fields.\"\u003cbr\u003e—Harald Niederreiter, \u003cem\u003eMathematical Reviews\u003c\/em\u003e, March 2014\u003c\/p\u003e\u003cp\u003e\"The book is filled with examples to illustrate the various constructions and, assuming a basic knowledge of combinatorics and algebraic geometry, it is almost self-contained.\"\u003cbr\u003e—Felipe Zaldivar, \u003cem\u003eMAA Reviews\u003c\/em\u003e, September 2013\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction to Algebraic Curves. Introduction to Error-Correcting Codes. Elliptic Curves and Their Applications to Cryptography. Secret Sharing Schemes. Authentication Codes. Frameproof Codes. Key Distribution Schemes. Broadcast Encryption and Multicast Security. Sequences.\u003cb\u003e \u003c\/b\u003eBibliography. Index.\u003c\/p\u003e","brand":"Taylor \u0026 Francis Ltd","offers":[{"title":"Default Title","offer_id":49408099615063,"sku":"9781420079463","price":99.75,"currency_code":"GBP","in_stock":true}]},{"product_id":"information-operations-9781574886993","title":"Information Operations","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe modern means of communication have turned the world into an information fishbowl and, in terms of foreign policy and national security in post-Cold War power politics, helped transform international power politics.","brand":"Potomac Books Inc","offers":[{"title":"Default Title","offer_id":49410295267671,"sku":"9781574886993","price":18.89,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781574886993.jpg?v=1730509723"},{"product_id":"new-directions-in-information-behaviour-9781780521701","title":"New Directions in Information Behaviour","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eNew Research in Information Behaviour, co-edited by Professor Amanda Spink and Dr. Jannica Heinstrom provides an understanding of the new directions, leading edge theories and models in information behaviour. Information behaviour is conceptualized as complex human information related processes that are embedded within an individual's everyday social and life processes. The book presents chapters by a range of scholars who show new research directions that often challenge the established views and paradigms of information behaviour studies. Beginning with an evolutionary framework, the book builds our understanding of information behaviours over various epochs of human existence from the Palaeolithic Era and within pre-literate societies, to contemporary behaviours by 21st century humans. Drawing upon social and psychological science theories the book presents a more integrated and holistic approach understanding of information behaviours. This book is directly relevant to information scientists, information professionals and librarians, social and evolutionary psychologists, social scientists and people interested in understanding more about their own information behaviours.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eList of Contributors. Preface.  Chapter 1 Introduction: New Directions in Information Behaviour.  Chapter 2 The Emergence of Conceptual Modelling in Information Behaviour Research.  Chapter 3 Meta-Synthesis with Information Behaviour Research.  Chapter 4 Weaving the Threads of Experience into Human Information Interaction (HII): Probing User Experience (UX) for New Directions in Information Behaviour.  Chapter 5 Into the Land of Adolescent Metacognitive Knowledge During the Information Search Process: A Metacognitive Ethnography.  Chapter 6 Individual Differences in Information-Related Behaviour: What Do We Know About Information Styles?.  Chapter 7 The Theory of Information Worlds and Information Behaviour.  Chapter 8 Towards Agency–Structure Integration: A Person-in-Environment (PIE) Framework for Modelling Individual-Level Information Behaviours and Outcomes.  Chapter 9 Understanding Casual-Leisure Information Behaviour.  Chapter 10 Information Behaviour Development in Early Childhood.  Chapter 11 Impacts of Information: An Analysis of Spiritual Messages.  Chapter 12 Conclusions and Further Research.  About the Authors.  Subject Index.  New Directions in Information Behaviour.  Library and Information Science.  Library and Information Science.  Copyright page.  Editorial Advisory Board.","brand":"Emerald Publishing Limited","offers":[{"title":"Default Title","offer_id":49411762684247,"sku":"9781780521701","price":96.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781780521701.jpg?v=1730514607"},{"product_id":"misbelief-9781785120800","title":"Misbelief","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003e'Timely... a crucial foundation for building a more empathetic and informed society.' - Daniel H. Pink\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e\u003cbr\u003e'An important book for those who want to understand... the increasingly complex world.' - Arianna Huffington\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e\u003cbr\u003eHow do we distinguish between fact and fiction in a post-truth world?\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eThe rise of fake news and AI-generated deep fakes have created a crisis of trust, making it more difficult than ever to know when we are being misled.\u003cbr\u003e\u003cbr\u003eRenowned social scientist Dan Ariely explores how the concept of misbelief can lead anyone to doubt established truth and embrace conspiracy theories. Drawing upon his first-hand experience of being the subject of disinformation, Ariely investigates the psychological drivers and motives behind irrational beliefs and what we do to counter them.\u003cbr\u003e\u003cbr\u003eAn urgent call-to-action, \u003ci\u003eMisbelief \u003c\/i\u003euncovers how we can stem the tide of misbelief with an empathetic and tolerant approach and restore trust in society.\u003c\/p\u003e","brand":"Bonnier Books Ltd","offers":[{"title":"Default Title","offer_id":49412155769175,"sku":"9781785120800","price":10.44,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781785120800.jpg?v=1730515841"}],"url":"https:\/\/bookcurl.com\/collections\/information-theory.oembed?page=7","provider":"Book Curl","version":"1.0","type":"link"}