Information Theory and Statistics

Author: Solomon Kullback
Publisher: Courier Corporation
ISBN: 0486142043
Category: Mathematics
Page: 416
View: 7791
Highly useful text studies logarithmic measures of information and their application to testing statistical hypotheses. Includes numerous worked examples and problems. References. Glossary. Appendix. 1968 2nd, revised edition.

Information Theory and Statistical Learning

Author: Frank Emmert-Streib,Matthias Dehmer
Publisher: Springer Science & Business Media
ISBN: 0387848150
Category: Computers
Page: 439
View: 7933
This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts.

Information Theory and Statistics

A Tutorial
Author: Imre Csiszár,Paul C. Shields
Publisher: Now Publishers Inc
ISBN: 9781933019055
Category: Computers
Page: 115
View: 8676
Information Theory and Statistics: A Tutorial is concerned with applications of information theory concepts in statistics, in the finite alphabet setting. The topics covered include large deviations, hypothesis testing, maximum likelihood estimation in exponential families, analysis of contingency tables, and iterative algorithms with an "information geometry" background. Also, an introduction is provided to the theory of universal coding, and to statistical inference via the minimum description length principle motivated by that theory. The tutorial does not assume the reader has an in-depth knowledge of Information Theory or statistics. As such, Information Theory and Statistics: A Tutorial, is an excellent introductory text to this highly-important topic in mathematics, computer science and electrical engineering. It provides both students and researchers with an invaluable resource to quickly get up to speed in the field.

Elements of Information Theory

Author: Thomas M. Cover,Joy A. Thomas
Publisher: John Wiley & Sons
ISBN: 1118585771
Category: Computers
Page: 776
View: 7511
The latest edition of this classic is updated with new problem setsand material The Second Edition of this fundamental textbook maintains thebook's tradition of clear, thought-provoking instruction. Readersare provided once again with an instructive mix of mathematics,physics, statistics, and information theory. All the essential topics in information theory are covered indetail, including entropy, data compression, channel capacity, ratedistortion, network information theory, and hypothesis testing. Theauthors provide readers with a solid understanding of theunderlying theory and applications. Problem sets and a telegraphicsummary at the end of each chapter further assist readers. Thehistorical notes that follow each chapter recap the mainpoints. The Second Edition features: * Chapters reorganized to improve teaching * 200 new problems * New material on source coding, portfolio theory, and feedbackcapacity * Updated references Now current and enhanced, the Second Edition of Elements ofInformation Theory remains the ideal textbook for upper-levelundergraduate and graduate courses in electrical engineering,statistics, and telecommunications. An Instructor's Manual presenting detailed solutions to all theproblems in the book is available from the Wiley editorialdepartment.

Basic concepts in information theory and statistics

axiomatic foundations and applications
Author: A. M. Mathai,P. N. Rathie
Publisher: Halsted Press
Category: Mathematics
Page: 137
View: 1618

Advances in Inequalities from Probability Theory and Statistics

Author: Neil S. Barnett,Sever Silvestru Dragomir
Publisher: Nova Publishers
ISBN: 9781600219436
Category: Inequalities (Mathematics)
Page: 227
View: 4788
This is the first in a series of research monographs that focus on the research, development and use of inequalities in probability and statistics. All of the papers have been peer refereed and this first edition covers a range of topics that include both survey material of published work as well as new results appearing in print for the first time.

Information Theory and the Central Limit Theorem

Author: Oliver Johnson
Publisher: World Scientific
ISBN: 9781860945373
Category: Computers
Page: 224
View: 9066
Annotation. - Presents surprising, interesting connections between two apparently separate areas of mathematics- Written by one of the researchers who discovered these connections- Offers a new way of looking at familiar results.

Information Theory for Continuous Systems

Author: Shunsuke Ihara
Publisher: World Scientific
ISBN: 9789810209858
Category: Computers
Page: 308
View: 8740
This book provides a systematic mathematical analysis of entropy and stochastic processes, especially Gaussian processes, and its applications to information theory.The contents fall roughly into two parts. In the first part a unified treatment of entropy in information theory, probability theory and mathematical statistics is presented. The second part deals mostly with information theory for continuous communication systems. Particular emphasis is placed on the Gaussian channel.An advantage of this book is that, unlike most books on information theory, it places emphasis on continuous communication systems, rather than discrete ones.

Quantum Information Theory and Quantum Statistics

Author: Dénes Petz
Publisher: Springer Science & Business Media
ISBN: 3540746366
Category: Science
Page: 216
View: 4489
This concise and readable book addresses primarily readers with a background in classical statistical physics and introduces quantum mechanical notions as required. Conceived as a primer to bridge the gap between statistical physics and quantum information, it emphasizes concepts and thorough discussions of the fundamental notions and prepares the reader for deeper studies, not least through a selection of well chosen exercises.

An Introduction to Information Theory

Author: Fazlollah M. Reza
Publisher: Courier Corporation
ISBN: 0486158446
Category: Mathematics
Page: 528
View: 6199
Graduate-level study for engineering students presents elements of modern probability theory, information theory, coding theory, more. Emphasis on sample space, random variables, capacity, etc. Many reference tables and extensive bibliography. 1961 edition.

Reliability Criteria in Information Theory and in Statistical Hypothesis Testing

Author: Evgueni A. Haroutunian,Mariam E. Haroutunian,Ashot N. Harutyunyan
Publisher: Now Publishers Inc
ISBN: 1601980469
Category: Computers
Page: 171
View: 5037
Reliability Criteria in Information Theory and Statistical Hypothesis Testing briefly formulates fundamental notions and results of Shannon theory on reliable transmission via coding and gives a survey of results obtained in last two-three decades by the authors, their colleagues and other researchers. It is essential reading for students, researchers and professionals working in Information Theory.

Statistical Theory Of Communication

Author: S.P. Eugene Xavier
Publisher: New Age International
ISBN: 9788122411270
Page: 508
View: 4935
This Book Deals With The Application Of Statistics To Communication Systems And Radar Signal Processing. Information Theory, Coding, Random Processes, Optimum Linear Systems And Estimation Theory Forms The Subject Matter Of This Book. The Subject Treatment Requires A Basic Knowledge Of Probability And Statistics. This Book Is Intended As A Text For A Graduate Level Course On Electronics And Communication Engineering.

Entropy and Information Theory

Author: Robert M. Gray
Publisher: Springer Science & Business Media
ISBN: 1475739826
Category: Computers
Page: 332
View: 4810
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.

Concepts of Probability Theory

Second Revised Edition
Author: Paul E. Pfeiffer
Publisher: Courier Corporation
ISBN: 0486165663
Category: Mathematics
Page: 416
View: 2343
Using the Kolmogorov model, this intermediate-level text discusses random variables, probability distributions, mathematical expectation, random processes, more. For advanced undergraduates students of science, engineering, or math. Includes problems with answers and six appendixes. 1965 edition.

Probability Theory and Statistical Inference

Econometric Modeling with Observational Data
Author: Aris Spanos
Publisher: Cambridge University Press
ISBN: 9780521424080
Category: Business & Economics
Page: 815
View: 4891
A major textbook for students taking introductory courses in probability theory and statistical inference.

Stochastic Models, Information Theory, and Lie Groups, Volume 2

Analytic Methods and Modern Applications
Author: Gregory S. Chirikjian
Publisher: Springer Science & Business Media
ISBN: 0817649433
Category: Mathematics
Page: 435
View: 8999
This two-volume set covers stochastic processes, information theory and Lie groups in a unified setting, bridging topics rarely studied together. The emphasis is on using stochastic, geometric, and group-theoretic concepts for modeling physical phenomena.

Statistical Inference Based on Divergence Measures

Author: Leandro Pardo
Publisher: CRC Press
ISBN: 9781420034813
Category: Mathematics
Page: 512
View: 7419
The idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models, many statisticians remain unaware of this powerful approach. Statistical Inference Based on Divergence Measures explores classical problems of statistical inference, such as estimation and hypothesis testing, on the basis of measures of entropy and divergence. The first two chapters form an overview, from a statistical perspective, of the most important measures of entropy and divergence and study their properties. The author then examines the statistical analysis of discrete multivariate data with emphasis is on problems in contingency tables and loglinear models using phi-divergence test statistics as well as minimum phi-divergence estimators. The final chapter looks at testing in general populations, presenting the interesting possibility of introducing alternative test statistics to classical ones like Wald, Rao, and likelihood ratio. Each chapter concludes with exercises that clarify the theoretical results and present additional results that complement the main discussions. Clear, comprehensive, and logically developed, this book offers a unique opportunity to gain not only a new perspective on some standard statistics problems, but the tools to put it into practice.

Theory of Games and Statistical Decisions

Author: David A. Blackwell,M. A. Girshick
Publisher: Courier Corporation
ISBN: 0486638316
Category: Mathematics
Page: 355
View: 1010
A problem-oriented text for evaluating statistical procedures through decision and game theory. First-year graduates in statistics, computer experts and others will find this highly respected work best introduction to growing field.

An Information-Theoretic Approach to Neural Computing

Author: Gustavo Deco,Dragan Obradovic
Publisher: Springer Science & Business Media
ISBN: 9780387946665
Category: Computers
Page: 261
View: 1846
Neural networks provide a powerful new technology to model and control nonlinear and complex systems. In this book, the authors present a detailed formulation of neural networks from the information-theoretic viewpoint. They show how this perspective provides new insights into the design theory of neural networks. In particular they show how these methods may be applied to the topics of supervised and unsupervised learning including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from several different scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this to be a very valuable introduction to this topic.