Classic Works of the Dempster-Shafer Theory of Belief Functions

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Yager, Roland R. (Επιμελητής έκδοσης), Liu, Liping (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
• Σειρά: Studies in Fuzziness and Soft Computing, 219
• Θέματα: Engineering.; Artificial intelligence.; Game theory.; Applied mathematics.; Engineering mathematics.; Economic theory.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).; Game Theory, Economics, Social and Behav. Sciences.; Economic Theory/Quantitative Economics/Mathematical Methods.

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....

Πίνακας περιεχομένων:

• 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.