Information Measures

Information and its Description in Science and Engineering

By: Christoph Arndt

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Paperback | 5 November 2003

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570 Pages

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23.5 x 15.88 x 3.18

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Information Measures introduces the mathematical description of information in science and engineering, treating the necessary mathematical theory in a more vivid way than in the usual theoretical proof structure. This enables readers to develop an idea of the connections between different information measures and to understand the trains of thoughts in their derivation. As there exist a great number of different possible ways to describe information, these measures are presented in a coherent manner. Examples of the measures treated: Shannon information, applied in coding theory; Akaike information criterion, used in system identification to determine auto-regressive models and in neural networks to identify the number of neurons; and Cramer-Rao bound or Fisher information, describing the minimal variances achieved by unbiased estimators. "A tool to show] bioinformaticians... how to handle immense amounts of raw data, such as are generated from genome mapping, make sense of them, and render them accessible to scientists." IEEE Engineering in Medicine and Biology

Industry Reviews

"Bioinformaticians are facing the challenge of how to handle immense amounts of raw data, such as are generated from genome mapping, make sense of them, and render them accessible to scientists working on a wide variety of problems. "Information Measures: Information and its Description in Science and Engineering" can be such a tool."

IEEE Engineering in Medicine and Biology

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Abstract
Introduction
Basic considerations
Historic development of information theory
The concept of entropy in physics
Extension of Shannon's information
Generalized entropy measures
Information functions and gaussian distributions
Shannon's information of discrete distributions
Information functions for gaussian distributions part II
Bounds of the variance
Ambiguity function
Akaike's information criterion
Channel information
Deterministic and stochastic information
Maximum entropy estimation
Concluding remarks
Appendix
Bibliography
Table of Contents provided by Publisher. All Rights Reserved.

Information Measures

Information and its Description in Science and Engineering

At a Glance

Paperback

Industry Reviews

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How to return your order

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