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Author: Ruslan L. Stratonovich Publisher: Springer Nature ISBN: 3030228339 Category : Mathematics Languages : en Pages : 432
Book Description
This English version of Ruslan L. Stratonovich’s Theory of Information (1975) builds on theory and provides methods, techniques, and concepts toward utilizing critical applications. Unifying theories of information, optimization, and statistical physics, the value of information theory has gained recognition in data science, machine learning, and artificial intelligence. With the emergence of a data-driven economy, progress in machine learning, artificial intelligence algorithms, and increased computational resources, the need for comprehending information is essential. This book is even more relevant today than when it was first published in 1975. It extends the classic work of R.L. Stratonovich, one of the original developers of the symmetrized version of stochastic calculus and filtering theory, to name just two topics. Each chapter begins with basic, fundamental ideas, supported by clear examples; the material then advances to great detail and depth. The reader is not required to be familiar with the more difficult and specific material. Rather, the treasure trove of examples of stochastic processes and problems makes this book accessible to a wide readership of researchers, postgraduates, and undergraduate students in mathematics, engineering, physics and computer science who are specializing in information theory, data analysis, or machine learning.
Author: Ruslan L. Stratonovich Publisher: Springer Nature ISBN: 3030228339 Category : Mathematics Languages : en Pages : 432
Book Description
This English version of Ruslan L. Stratonovich’s Theory of Information (1975) builds on theory and provides methods, techniques, and concepts toward utilizing critical applications. Unifying theories of information, optimization, and statistical physics, the value of information theory has gained recognition in data science, machine learning, and artificial intelligence. With the emergence of a data-driven economy, progress in machine learning, artificial intelligence algorithms, and increased computational resources, the need for comprehending information is essential. This book is even more relevant today than when it was first published in 1975. It extends the classic work of R.L. Stratonovich, one of the original developers of the symmetrized version of stochastic calculus and filtering theory, to name just two topics. Each chapter begins with basic, fundamental ideas, supported by clear examples; the material then advances to great detail and depth. The reader is not required to be familiar with the more difficult and specific material. Rather, the treasure trove of examples of stochastic processes and problems makes this book accessible to a wide readership of researchers, postgraduates, and undergraduate students in mathematics, engineering, physics and computer science who are specializing in information theory, data analysis, or machine learning.
Author: JV Stone Publisher: Sebtel Press ISBN: 0956372856 Category : Business & Economics Languages : en Pages : 243
Book Description
Originally developed by Claude Shannon in the 1940s, information theory laid the foundations for the digital revolution, and is now an essential tool in telecommunications, genetics, linguistics, brain sciences, and deep space communication. In this richly illustrated book, accessible examples are used to introduce information theory in terms of everyday games like ‘20 questions’ before more advanced topics are explored. Online MatLab and Python computer programs provide hands-on experience of information theory in action, and PowerPoint slides give support for teaching. Written in an informal style, with a comprehensive glossary and tutorial appendices, this text is an ideal primer for novices who wish to learn the essential principles and applications of information theory.
Author: Erich H. Rast Publisher: Rowman & Littlefield ISBN: 1793616957 Category : Philosophy Languages : en Pages : 234
Book Description
The theory of value structure concerns the meaning of “better than” and “good,” as well as the way in which values serve as a basis for rational decision making. Drawing methodologically from economics and theories of decision making, the aim of serious axiology in metaethics is to do justice to problems that have puzzled philosophers of value for centuries. Can value comparisons be cyclic? Are all values comparable with each other and can decision makers just add up different aspects of an evaluation to determine the best course of action? A Theory of Value Structure: From Values to Decisions starts with a thorough introduction to the modeling of “better than” comparisons from a normative perspective. In the philosophical part of the book, Erich H. Rast argues that aspects of “better than” comparisons can differ qualitatively so much that one aspect may outrank another. Consequently, the classical weighted sum aggregation model fails. Values cannot always be summed up and comparisons may be fundamentally noncompensatory, an indeterminacy that explains problems like the apparent nontransitivity of “better than” and hard cases in decision making. Using a lexicographic method of value comparisons, Rast develops a multidimensional theory of “better than” and shows how and to which extent it can be combined with standard methods of decision making under uncertainty by using rank-dependent utility theory.
Author: David Ellerman Publisher: Springer Nature ISBN: 3030865525 Category : Philosophy Languages : en Pages : 121
Book Description
This monograph offers a new foundation for information theory that is based on the notion of information-as-distinctions, being directly measured by logical entropy, and on the re-quantification as Shannon entropy, which is the fundamental concept for the theory of coding and communications. Information is based on distinctions, differences, distinguishability, and diversity. Information sets are defined that express the distinctions made by a partition, e.g., the inverse-image of a random variable so they represent the pre-probability notion of information. Then logical entropy is a probability measure on the information sets, the probability that on two independent trials, a distinction or “dit” of the partition will be obtained. The formula for logical entropy is a new derivation of an old formula that goes back to the early twentieth century and has been re-derived many times in different contexts. As a probability measure, all the compound notions of joint, conditional, and mutual logical entropy are immediate. The Shannon entropy (which is not defined as a measure in the sense of measure theory) and its compound notions are then derived from a non-linear dit-to-bit transform that re-quantifies the distinctions of a random variable in terms of bits—so the Shannon entropy is the average number of binary distinctions or bits necessary to make all the distinctions of the random variable. And, using a linearization method, all the set concepts in this logical information theory naturally extend to vector spaces in general—and to Hilbert spaces in particular—for quantum logical information theory which provides the natural measure of the distinctions made in quantum measurement. Relatively short but dense in content, this work can be a reference to researchers and graduate students doing investigations in information theory, maximum entropy methods in physics, engineering, and statistics, and to all those with a special interest in a new approach to quantum information theory.
Author: Thomas M. Cover Publisher: John Wiley & Sons ISBN: 1118585771 Category : Computers Languages : en Pages : 788
Book Description
The latest edition of this classic is updated with new problem sets and material 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. 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. The Second Edition features: * Chapters reorganized to improve teaching * 200 new problems * New material on source coding, portfolio theory, and feedback capacity * Updated references Now current and enhanced, the Second Edition of Elements of Information Theory remains the ideal textbook for upper-level undergraduate and graduate courses in electrical engineering, statistics, and telecommunications.
Author: Christopher G. Timpson Publisher: Oxford Philosophical Monograph ISBN: 0199296464 Category : Computers Languages : en Pages : 308
Book Description
Christopher G. Timpson provides the first full-length philosophical treatment of quantum information theory and the questions it raises for our understanding of the quantum world. He argues for an ontologically deflationary account of the nature of quantum information, which is grounded in a revisionary analysis of the concepts of information.
Author: Jeroen van den Hoven Publisher: Cambridge University Press ISBN: 9780521671613 Category : Philosophy Languages : en Pages : 428
Book Description
This book gives an in-depth philosophical analysis of moral problems to which information technology gives rise, for example, problems related to privacy, intellectual property, responsibility, friendship, and trust, with contributions from many of the best-known philosophers writing in the area.
Author: Yogesh K. Dwivedi Publisher: Springer Science & Business Media ISBN: 1441961089 Category : Business & Economics Languages : en Pages : 515
Book Description
The overall mission of this book is to provide a comprehensive understanding and coverage of the various theories and models used in IS research. Specifically, it aims to focus on the following key objectives: To describe the various theories and models applicable to studying IS/IT management issues. To outline and describe, for each of the various theories and models, independent and dependent constructs, reference discipline/originating area, originating author(s), seminal articles, level of analysis (i.e. firm, individual, industry) and links with other theories. To provide a critical review/meta-analysis of IS/IT management articles that have used a particular theory/model. To discuss how a theory can be used to better understand how information systems can be effectively deployed in today’s digital world. This book contributes to our understanding of a number of theories and models. The theoretical contribution of this book is that it analyzes and synthesizes the relevant literature in order to enhance knowledge of IS theories and models from various perspectives. To cater to the information needs of a diverse spectrum of readers, this book is structured into two volumes, with each volume further broken down into two sections. The first section of Volume 1 presents detailed descriptions of a set of theories centered around the IS lifecycle, including the Success Model, Technology Acceptance Model, User Resistance Theories, and four others. The second section of Volume 1 contains strategic and economic theories, including a Resource-Based View, Theory of Slack Resources, Portfolio Theory, Discrepancy Theory Models, and eleven others. The first section of Volume 2 concerns socio-psychological theories. These include Personal Construct Theory, Psychological Ownership, Transactive Memory, Language-Action Approach, and nine others. The second section of Volume 2 deals with methodological theories, including Critical Realism, Grounded Theory, Narrative Inquiry, Work System Method, and four others. Together, these theories provide a rich tapestry of knowledge around the use of theory in IS research. Since most of these theories are from contributing disciplines, they provide a window into the world of external thought leadership.
Author: Robert M. Gray Publisher: Springer Science & Business Media ISBN: 1475739826 Category : Computers Languages : en Pages : 346
Book Description
This book is devoted to the theory of probabilistic information measures and their application to coding theorems for information sources and noisy channels. The eventual goal is a general development of Shannon's mathematical theory of communication, but much of the space is devoted to the tools and methods required to prove the Shannon coding theorems. These tools form an area common to ergodic theory and information theory and comprise several quantitative notions of the information in random variables, random processes, and dynamical systems. Examples are entropy, mutual information, conditional entropy, conditional information, and discrimination or relative entropy, along with the limiting normalized versions of these quantities such as entropy rate and information rate. Much of the book is concerned with their properties, especially the long term asymptotic behavior of sample information and expected information. This is the only up-to-date treatment of traditional information theory emphasizing ergodic theory.
Author: James Gleick Publisher: Vintage ISBN: 0307379574 Category : Science Languages : en Pages : 398
Book Description
From the bestselling author of the acclaimed Chaos and Genius comes a thoughtful and provocative exploration of the big ideas of the modern era: Information, communication, and information theory. Acclaimed science writer James Gleick presents an eye-opening vision of how our relationship to information has transformed the very nature of human consciousness. A fascinating intellectual journey through the history of communication and information, from the language of Africa’s talking drums to the invention of written alphabets; from the electronic transmission of code to the origins of information theory, into the new information age and the current deluge of news, tweets, images, and blogs. Along the way, Gleick profiles key innovators, including Charles Babbage, Ada Lovelace, Samuel Morse, and Claude Shannon, and reveals how our understanding of information is transforming not only how we look at the world, but how we live. A New York Times Notable Book A Los Angeles Times and Cleveland Plain Dealer Best Book of the Year Winner of the PEN/E. O. Wilson Literary Science Writing Award
Author: Ruslan L. Stratonovich
Author: Ruslan L. Stratonovich
Author: JV Stone
Author: Erich H. Rast
Author: David Ellerman
Author: Thomas M. Cover
Author: Christopher G. Timpson
Author: Jeroen van den Hoven
Author: Yogesh K. Dwivedi
Author: Robert M. Gray
Author: James Gleick