Uncertainty-Based Information
Elements of Generalized Information Theory
Published on 19. February 1998
Book
Hardback
XVI, 168 pages
978-3-7908-1073-8 (ISBN)
Description
The book is an overview of the development of basic ideas and mathematical results regarding measures and principles of uncertainty-based information formalized within the framework of classical set theory, probability theory, fuzzy set theory, possibility theory, and the Dempster-Shafer theory of evidence.
The book contains many new results, which had until now not been available in a single monograph. The book is very useful for researchers, but it can also be used as a graduate text.
The book contains many new results, which had until now not been available in a single monograph. The book is very useful for researchers, but it can also be used as a graduate text.
More details
Series
Language
English
Place of publication
Heidelberg
Germany
Target group
College/higher education
Professional and scholarly
Illustrations
10
11 s/w Abbildungen, 10 s/w Tabellen
11 figures, 10 tables
Dimensions
Height: 23.5 cm
Width: 15.5 cm
Weight
380 gr
ISBN-13
978-3-7908-1073-8 (9783790810738)
Schweitzer Classification
Other editions
New editions
George J. Klir | Mark J. Wierman
Uncertainty-Based Information
Elements of Generalized Information Theory
Book
10/1999
2nd Edition
Physica
€106.99
Shipment within 10-15 days
Content
Introduction: Significance of Uncertainty; Uncertainty and Information.- Uncertainty Formalizations: Classical Sets: Terminology and Notation; Fuzzy Set Theory. Fuzzy Operations. Fuzzy Subsethood. Cylindric Extensions. Types of Fuzzy Sets; Fuzzy Measure Theory; Evidence Theory. Upper and Lower Probabilities; Probability Theory; Possibility Theory; Overview of Uncertainty Theories.- Uncertainty Measures: Nonspecifity. Hartley Function. U-uncertainty. Nonspecifity in Evidence Theory. Nonspecifity of Sets in n-Dimensional Euclidean Space. Generalized Hartley-Like Measures of Nonspecifity ; Conflict. Shannon Entropy. Entropy-Like Measure in Evidence Theory. Conflict in Possibility Theory; Aggregate Uncertainty in Evidence Theory. General Algorithm. for Computing Function AU. Computing Function AU in Possibility Theory; Fuzziness; Summary of Uncertainty Measures.- Principles of Uncertainty: Principle of Minimum Uncertainty; Principle of Maximum Uncertainty; Principle of Uncertainty Invariance. Probability-Possibility Transformations. Approximations of Fuzzy Sets. Approximations in Evidence Theory. Revised Probability-Possibility Transformations; Summary of Uncertainty Principles.- Conclusions: Appraisal of Current Results; Unresolved Problems; Future Directions.