Information Measures || Information functions and gaussian distributions
β Scribed by Arndt, Christoph
- Book ID
- 120330055
- Publisher
- Springer Berlin Heidelberg
- Year
- 2001
- Tongue
- English
- Weight
- 745 KB
- Edition
- 1
- Category
- Article
- ISBN
- 3642566693
No coin nor oath required. For personal study only.
β¦ Synopsis
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.
π SIMILAR VOLUMES
This article defines the concept of an information measure and shows how common information measures such as entropy, Shannon information, and algorithmic information content can be combined to solve problems of characterization, inference, and learning for complex systems. Particularly useful quant