Geared toward upper-level undergraduates and graduate students, this classic resource by a giant of 20th-century mathematics applies principles of information theory to Maxwell's demon, thermodynamics, and measurement problems. 1962 edition.

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.

Algorithmic information theory (AIT), or Kolmogorov complexity as it is known to mathematicians, can provide a useful tool for scientists to look at natural systems, however, some critical conceptual issues need to be understood and the advances already made collated and put in a form accessible to scientists. This book has been written in the hope that readers will be able to absorb the key ideas behind AIT so that they are in a better position to access the mathematical developments and to apply the ideas to their own areas of interest. The theoretical underpinning of AIT is outlined in the earlier chapters, while later chapters focus on the applications, drawing attention to the thermodynamic commonality between ordered physical systems such as the alignment of magnetic spins, the maintenance of a laser distant from equilibrium, and ordered living systems such as bacterial systems, an ecology, and an economy. Key Features Presents a mathematically complex subject in language accessible to scientists Provides rich insights into modelling far-from-equilibrium systems Emphasises applications across range of fields, including physics, biology and econophysics Empowers scientists to apply these mathematical tools to their own research

This book deals with information theory spanning across different areas of science such as Quantum Physics, Quantum Computing, Genetics, Thermodynamics. It describes how information underlies everything in the universe and the relationship between entropy of the universe and information. it takes into account Shannon's information theory introduced in 1948 and discusses contemprerary developments in the information theory field. it discusses how the universe originated from bigbang and how life evolved for the last 4.5 billion years that the Earth was formed. More over, it deals with new concepts in the field of quantum physics like quantum entanglement and quantum tunneling . it also discusses the possibilities of sending information at the speed of light or more than that. it describes the Boltzmann's equations for stastical systems, maxwell's daemon, turing machine. this book touches upon a little bit of mathematic equations to explain about the quantum computing, how to build quantum logic gates and further developments in that field . it talks about astronomical objects like blackholes, quasars and the thermo-dynamics operating behind them. it touches upon genetics as genes carry a huge amount of information in their DNA to shape up an organism.

In this highly readable book, H.S. Green, a former student of Max Born and well known as an author in physics and in the philosophy of science, presents a timely analysis of theoretical physics and related fundamental problems.

First comprehensive introduction to information theory explores the work of Shannon, McMillan, Feinstein, and Khinchin. Topics include the entropy concept in probability theory, fundamental theorems, and other subjects. 1957 edition.

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.