Information Theory And Selected Applications
Download Information Theory And Selected Applications PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Information Theory And Selected Applications book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Information Theory and Selected Applications
This book focuses on analysing the applications of the Shannon Measure of Information (SMI). The book introduces the concept of frustration and discusses the question of the quantification of this concept within information theory (IT), while it also focuses on the interpretation of the entropy of systems of interacting particles in terms of the SMI and of mutual information. The author examines the question of the possibility of measuring the extent of frustration using mutual information and discusses some classical examples of processes of mixing and assimilation for which the entropy changes are interpreted in terms of SMI. A description of a few binding systems and the interpretation of cooperativity phenomena in terms of mutual information are also presented, along with a detailed discussion on the general method of using maximum SMI in order to find the “best-guess” probability distribution. This book is a valuable contribution to the field of information theory and will be of great interest to any scientist who is interested in IT and in its potential applications.
Information Theory and Selected Applications
This book focuses on analysing the applications of the Shannon Measure of Information (SMI). The book introduces the concept of frustration and discusses the question of the quantification of this concept within information theory (IT), while it also focuses on the interpretation of the entropy of systems of interacting particles in terms of the SMI and of mutual information. The author examines the question of the possibility of measuring the extent of frustration using mutual information and discusses some classical examples of processes of mixing and assimilation for which the entropy changes are interpreted in terms of SMI. A description of a few binding systems and the interpretation of cooperativity phenomena in terms of mutual information are also presented, along with a detailed discussion on the general method of using maximum SMI in order to find the "best-guess" probability distribution. This book is a valuable contribution to the field of information theory and will be of great interest to any scientist who is interested in IT and in its potential applications.
Information Theory, Inference and Learning Algorithms
Author: David J. C. MacKay
language: en
Publisher: Cambridge University Press
Release Date: 2003-09-25
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 communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.