Classic works of the Dempster-Shafer theory of belief functions
This book brings together a collection of classic research papers on the Dempster-Shafer theory of belief functions. By bridging fuzzy logic and probabilistic reasoning, the theory of belief functions has become a primary tool for knowledge representation and uncertainty reasoning in expert systems....
| Other Authors: | Yager, Ronald R., 1941-, Liu, Liping (Computer scientist), SpringerLink (Online service) |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Berlin ; New York :
Springer,
2008.
Berlin ; New York : 2008. |
| Physical Description: |
1 online resource (xix, 806 pages) : illustrations. |
| Series: |
Studies in fuzziness and soft computing ;
v. 219. |
| Subjects: |
Table of Contents:
- 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.