Information Theory Inference And Learning Algorithms

Information Theory  Inference and Learning Algorithms
Publisher Cambridge University Press
Release Date
Category Computers
Total Pages 628
ISBN 0521642981
Rating 4.5/5 from 10 reviews
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Table of contents

Information Theory  Inference and Learning Algorithms
  • Author : David J. C. MacKay,David J. C. Mac Kay
  • Publisher : Cambridge University Press
  • Release Date : 2003-09-25

Table of contents

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Information Theory   Inference And Learning Algorithms
  • Author : MACKAY
  • Publisher : Unknown
  • Release Date : 2021-07-30

Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical

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Information Theory
  • Author : JV Stone
  • Publisher : Sebtel Press
  • Release Date : 2015-01-01

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

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Information Theory and Statistical Learning
  • Author : Frank Emmert-Streib,Matthias Dehmer
  • Publisher : Springer Science & Business Media
  • Release Date : 2009

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.

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A Student s Guide to Coding and Information Theory
  • Author : Stefan M. Moser,Po-Ning Chen
  • Publisher : Cambridge University Press
  • Release Date : 2012-01-26

A concise, easy-to-read guide, introducing beginners to the engineering background of modern communication systems, from mobile phones to data storage. Assuming only basic knowledge of high-school mathematics and including many practical examples and exercises to aid understanding, this is ideal for anyone who needs a quick introduction to the subject.

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An Introduction to Information Theory
  • Author : Fazlollah M. Reza
  • Publisher : Courier Corporation
  • Release Date : 2012-07-13

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.

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Algorithmic Information Theory
  • Author : Peter Seibt
  • Publisher : Springer Science & Business Media
  • Release Date : 2007-02-15

Algorithmic Information Theory treats the mathematics of many important areas in digital information processing. It has been written as a read-and-learn book on concrete mathematics, for teachers, students and practitioners in electronic engineering, computer science and mathematics. The presentation is dense, and the examples and exercises are numerous. It is

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Information Theory and Statistics
  • Author : Solomon Kullback
  • Publisher : Courier Corporation
  • Release Date : 2012-09-11

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.

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Elements of Information Theory
  • Author : Thomas M. Cover,Joy A. Thomas
  • Publisher : John Wiley & Sons
  • Release Date : 2012-11-28

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

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Multiple Classifier Systems
  • Author : Jón Atli Benediktsson,Josef Kittler,Fabio Roli
  • Publisher : Springer Science & Business Media
  • Release Date : 2009-06-02

This book constitutes the refereed proceedings of the 8th International Workshop on Multiple Classifier Systems, MCS 2009, held in Reykjavik, Iceland, in June 2009. The 52 revised full papers presented together with 2 invited papers were carefully reviewed and selected from more than 70 initial submissions. The papers are organized in topical sections on ECOC

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Information Theory
  • Author : Imre Csiszár,János Körner
  • Publisher : Elsevier
  • Release Date : 2014-07-10

Information Theory: Coding Theorems for Discrete Memoryless Systems presents mathematical models that involve independent random variables with finite range. This three-chapter text specifically describes the characteristic phenomena of information theory. Chapter 1 deals with information measures in simple coding problems, with emphasis on some formal properties of Shannon’s information and

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Entropy and Information Theory
  • Author : Robert M. Gray
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-03-14

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

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Introduction to Coding and Information Theory
  • Author : Steven Roman
  • Publisher : Springer Science & Business Media
  • Release Date : 1996-11-26

This book is intended to introduce coding theory and information theory to undergraduate students of mathematics and computer science. It begins with a review of probablity theory as applied to finite sample spaces and a general introduction to the nature and types of codes. The two subsequent chapters discuss information

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Probabilistic Graphical Models
  • Author : Daphne Koller,Nir Friedman
  • Publisher : MIT Press
  • Release Date : 2009-07-31

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in

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Bayesian Reasoning and Machine Learning
  • Author : David Barber
  • Publisher : Cambridge University Press
  • Release Date : 2012-02-02

A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

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