Chapter 3. Classical Probability-Based Uncertainty Theory
Published Online: 7 NOV 2005
DOI: 10.1002/0471755575.ch3
Copyright © 2006 John Wiley & Sons, Inc.
Book Title
Uncertainty and Information: Foundations of Generalized Information Theory
Additional Information
How to Cite
Klir, G. J. (2005) Classical Probability-Based Uncertainty Theory, in Uncertainty and Information: Foundations of Generalized Information Theory, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/0471755575.ch3
Publication History
- Published Online: 7 NOV 2005
- Published Print: 4 NOV 2005
ISBN Information
Print ISBN: 9780471748670
Online ISBN: 9780471755579
- Summary
- Chapter
Keywords:
- probability functions on finite and infinite sets;
- Bayes' theorem;
- Shannon entropy;
- Shannon cross-entropy
Summary
Basic ideas of classical probability theory are introduced. Shannon entropy, as a measure of probabilistic uncertainty on finite sets, is derived and its uniqueness is established. The issue of how to measure probabilistic uncertainty for infinite sets is discussed.