Classic Works of the Dempster-Shafer Theory of Belief Functions

2008
ISBN: 978-3-540-25381-5
Verlag: Springer Berlin Heidelberg

Buch, Englisch, Band 219, 806 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1390 g

Reihe: Studies in Fuzziness and Soft Computing

This is a collection of classic research papers on the Dempster-Shafer theory of belief functions. The book is the authoritative reference in the field of evidential reasoning and an important archival reference in a wide range of areas including uncertainty reasoning in artificial intelligence and decision making in economics, engineering, and management. The book includes a foreword reflecting the development of the theory in the last forty years.

Zielgruppe

Research

Autoren/Hrsg.

Liu, Liping

Yager, Ronald R.

Fachgebiete

• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftstheorie, Wirtschaftsphilosophie
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik

Inhaltsverzeichnis

Classic Works of the Dempster-Shafer Theory of Belief Functions: An Introduction.- New Methods for Reasoning Towards Posterior Distributions Based on Sample Data.- Upper and Lower Probabilities Induced by a Multivalued Mapping.- A Generalization of Bayesian Inference.- On Random Sets and Belief Functions.- Non-Additive Probabilities in the Work of Bernoulli and Lambert.- Allocations of Probability.- Computational Methods for A Mathematical Theory of Evidence.- Constructive Probability.- Belief Functions and Parametric Models.- Entropy and Specificity in a Mathematical Theory of Evidence.- A Method for Managing Evidential Reasoning in a Hierarchical Hypothesis Space.- Languages and Designs for Probability Judgment.- A Set-Theoretic View of Belief Functions.- Weights of Evidence and Internal Conflict for Support Functions.- A Framework for Evidential-Reasoning Systems.- Epistemic Logics, Probability, and the Calculus of Evidence.- Implementing Dempster’s Rule for Hierarchical Evidence.- Some Characterizations of Lower Probabilities and Other Monotone Capacities through the use of Möbius Inversion.- Axioms for Probability and Belief-Function Propagation.- Generalizing the Dempster–Shafer Theory to Fuzzy Sets.- Bayesian Updating and Belief Functions.- Belief-Function Formulas for Audit Risk.- Decision Making Under Dempster–Shafer Uncertainties.- Belief Functions: The Disjunctive Rule of Combination and the Generalized Bayesian Theorem.- Representation of Evidence by Hints.- Combining the Results of Several Neural Network Classifiers.- The Transferable Belief Model.- A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory.- Logicist Statistics II: Inference.