Evidence Theory and Its Application: v. 1
Published on 22. August 1991
Book
Hardback
364 pages
978-0-444-88962-1 (ISBN)
Description
This book presents an easy-to-read, updated evidence theory and its applications, built on the Dempster-Shafer theory. The Dempster-Shafer theory significantly generalizes classic Bayesian statistics, and has been rapidly developed recently because of its many found applications in a variety of areas such as artificial intelligence, expert systems, information systems, decision making, statistics and mathematics. The volume gives an introduction to the Dempster-Shafer theory, introduces Barnett's methodology to linearize the time complexity of computation of evidential functions, discusses separable mass functions, and deals with rule strengths in expert systems. This volume is intended for a wide readership, ranging from academics, to engineers, system developers and managers in information systems, computer science, and business management. The guide is adaptable for both lectures and self-study and is intended to strengthen the reader's background and problem solving abilities.
This book presents an easy-to-read, updated evidence theory and its applications, built on the Dempster-Shafer theory. The Dempster-Shafer theory significantly generalizes classic Bayesian statistics, and has been rapidly developed recently because of its many found applications in a variety of areas such as artificial intelligence, expert systems, information systems, decision making, statistics and mathematics. The volume gives an introduction to the Dempster-Shafer theory, introduces Barnett's methodology to linearize the time complexity of computation of evidential functions, discusses separable mass functions, and deals with rule strengths in expert systems. This volume is intended for a wide readership, ranging from academics, to engineers, system developers and managers in information systems, computer science, and business management. The guide is adaptable for both lectures and self-study and is intended to strengthen the reader's background and problem solving abilities.
This book presents an easy-to-read, updated evidence theory and its applications, built on the Dempster-Shafer theory. The Dempster-Shafer theory significantly generalizes classic Bayesian statistics, and has been rapidly developed recently because of its many found applications in a variety of areas such as artificial intelligence, expert systems, information systems, decision making, statistics and mathematics. The volume gives an introduction to the Dempster-Shafer theory, introduces Barnett's methodology to linearize the time complexity of computation of evidential functions, discusses separable mass functions, and deals with rule strengths in expert systems. This volume is intended for a wide readership, ranging from academics, to engineers, system developers and managers in information systems, computer science, and business management. The guide is adaptable for both lectures and self-study and is intended to strengthen the reader's background and problem solving abilities.
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Elsevier Science & Technology
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 230 mm
ISBN-13
978-0-444-88962-1 (9780444889621)
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Schweitzer Classification
Content
Chapter 1. Finite Sets. 1.1. Set Operations and Mappings. 1.2. Finite Sets. 1.3. Mobius Inversions. Chapter 2. Evidential Functions. 2.1. Bayesian Statistics. 2.2. Mass Functions and Belief Functions. 2.3. Commonality Functions. 2.4. Plausibility Functions. Chapter 3. Dempster-Shafer's Rule. 3.1. Combining Mass Functions. 3.2. Some Application Examples. 3.3. The Basic Properties of the Orthogonal Sum. 3.4. Combining Evidential Functions. 3.5. Conditional Probabilities. Chapter 4. Separable Support Functions. 4.1. Simple Support Functions. 4.2. Separable Support Functions. Chapter 5. Decomposing Mass Functions. 5.1. Decomposing Mass Functions. 5.2. Weight assessment was . Chapter 6. The Canonical Decomposition. 6.1. Inversion between com and was . 6.2. Canonical Decomposition of Mass Functions. Chapter 7. The Separability of Mass Functions. 7.1. A Criterion for Separability. 7.2. Impingement Quasi-functions. 7.3. Internal Conflict Weights. Chapter 8. Linear Time Computations. 8.1. Barnett's Structure. 8.2. Algorithms and Computations. 8.3. Conflict and Decisiveness. Chapter 9. Rule Strengths in Expert Systems. 9.1. The Dempster-Shafer Theory. 9.2. The Extended Dempster-Shafer Theory. 9.3. Yen's Method for Reasoning. 9.4. A Direct Way of Reasoning in a Single Step. Chapter 10. 10.1. Rule Strengths in Expert Systems. 10.2. Data and Evidence. 10.3. Hypothesis Strength. 10.4. Combining Prior Mass and Different Rules. 10.5. Belief Interval and Ignorance. Bibliography. Index.