Entropy Measures, Maximum Entropy Principle and Emerging Applications

1. Front Matter

   Pages I-X

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2. Uncertainty, Entropy and Maximum Entropy Principle — An Overview

   • Karmeshu, N. R. Pal

   Pages 1-53

3. Facets of Generalized Uncertainty-based Information

   • George J. Klir

   Pages 55-78

4. Application of the Maximum (Information) Entropy Principle to Stochastic Processes far from Thermal Equilibrium

   • Hermann Haken

   Pages 79-89

5. Maximum Entropy Principle, Information of Non-Random Functions and Complex Fractals

   • Guy Jumarie

   Pages 91-101

6. Geometric Ideas in Minimum Cross-Entropy

   • L. Lore Campbell

   Pages 103-114

7. Information-Theoretic Measures for Knowledge Discovery and Data Mining

   • Y. Y. Yao

   Pages 115-136

8. A Universal Maximum Entropy Solution for Complex Queueing Systems and Networks

   • Demetres Kouvatsos

   Pages 137-162

9. Minimum Mean Deviation from the Steady-State Condition in Queueing Theory

   • Silviu Guiasu

   Pages 163-175

10. On the Utility of Different Entropy Measures in Image Thresholding

    • D. P. Mandal, N. R. Pal

    Pages 177-198

11. Entropic Thresholding Algorithms and their Optimizations

    • C. H. Li, C. K. Lee, P. K. S. Tam

    Pages 199-208

12. Entropy and Complexity of Sequences

    • Werner Ebeling, Miguel Jimenez-Montano, Thomas Pohl

    Pages 209-227

13. Some Lessons for Molecular Biology from Information Theory

    • Thomas D. Schneider

    Pages 229-237

14. Computation of the MinMax Measure

    • M. Srikanth, H. K. Kesavan, Peter Roe

    Pages 239-251

15. On Three Functional Equations Related to the Bose-Einstein Entropy

    • PL. Kannappan, P. K. Sahoo

    Pages 253-259

16. The Entropy Theory as a Decision Making Tool in Environmental and Water Resources

    • Vijay P. Singh

    Pages 261-297

The last two decades have witnessed an enormous growth with regard to ap­ plications of information theoretic framework in areas of physical, biological, engineering and even social sciences. In particular, growth has been spectac­ ular in the field of information technology,soft computing,nonlinear systems and molecular biology. Claude Shannon in 1948 laid the foundation of the field of information theory in the context of communication theory. It is in­ deed remarkable that his framework is as relevant today as was when he 1 proposed it. Shannon died on Feb 24, 2001. Arun Netravali observes "As if assuming that inexpensive, high-speed processing would come to pass, Shan­ non figured out the upper limits on communication rates. First in telephone channels, then in optical communications, and now in wireless, Shannon has had the utmost value in defining the engineering limits we face". Shannon introduced the concept of entropy. The notable feature of the entropy frame­ work is that it enables quantification of uncertainty present in a system. In many realistic situations one is confronted only with partial or incomplete information in the form of moment, or bounds on these values etc. ; and it is then required to construct a probabilistic model from this partial information. In such situations, the principle of maximum entropy provides a rational ba­ sis for constructing a probabilistic model. It is thus necessary and important to keep track of advances in the applications of maximum entropy principle to ever expanding areas of knowledge.

• Applied Information Theory
• Entropy Optimization
• Fuzzy
• Information
• Information Measures
• Maximum Entropy Principle
• Shannon
• algorithm
• algorithms
• calculus
• data mining
• entropy
• geometry
• information theory
• optimization
• complexity

• School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India

  Karmeshu

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