Information Measures

From the reviews: "Bioinformaticians are facing the challenge of how to handle immense amounts of raw data, [ ] and render them accessible to scientists working on a wide variety of problems. [This book] can be such a tool." IEEE Engineering in Medicine and BiologyThis book is an introduction to the mathematical description of information in science and engineering. The necessary mathematical theory will be treated in a more vivid way than in the usual theorem-proof structure. This enables the reader 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. Some examples of the information measures examined are: 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 neu-rons; and Cramer-Rao bound or Fisher information, describing the minimal variances achieved by unbiased estimators. This softcover edition addresses researchers and students in electrical engineering, particularly in control and communications, physics, and applied mathematics.