| Information Measures: Information and Its Description in Science and Engineering 2001 Edition Contributor(s): Arndt, Christoph (Author) |
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ISBN: 3540416331 ISBN-13: 9783540416333 Publisher: Springer OUR PRICE: $161.49 Product Type: Hardcover - Other Formats Published: March 2001 Annotation: This book is an introduction to the mathematical description of information in science and engineering. The necessary ma- thematical theory will be treated in a more vivid way than in the usual theoretical proof structure. This enables the reader to develop an idea of the connections between diffe- rent 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. Some examples of the information measures examined are: Shannon informati- on, 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 neu- rons; and Cramer-Rao bound or Fisher information, describing the minimal variances achieved by unbiased estimators. |
| Additional Information |
| BISAC Categories: - Science | Reference - Technology & Engineering | Electrical - Computers | Information Theory |
| Dewey: 003.54 |
| LCCN: 2001020510 |
| Physical Information: 1.25" H x 6.14" W x 9.21" (2.14 lbs) 548 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: This book is intended to be an introduction to the mathematical description of information in science. The necessary mathematical theory of this introduction will be treated in a more vivid way than in the usual theorem-proof structure. This, however, enables us to develop an idea of the connections between different information measures and to understand the trains of thought in their derivation, which is a crucial point for correct applications. It is therefore our intention in the mathematical descriptions to evolve the important ideas of the derivations, so that we obtain the resulting functions as well as the main thoughts and the conditions for the validity of the result. This simplifies the handling of the information measures, which are sometimes hard to classify without any additional background information. Though the mathematical descriptions are the exact formulations of the measures examined, we do not restrict ourselves to rigorous mathematical considerations, but we will also integrate the different measures into the structure and context of possible information measures. Nevertheless the mathematical approach is unavoidable when we are looking for an objective description and for possible applications in optimization. |