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Classic Works of the Dempster-Shafer Theory of Belief Functions

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Table of Contents

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    Book Overview
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    Chapter 1 Classic Works of the Dempster-Shafer Theory of Belief Functions: An Introduction
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    Chapter 2 New Methods for Reasoning Towards Posterior Distributions Based on Sample Data
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    Chapter 3 Upper and Lower Probabilities Induced by a Multivalued Mapping
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    Chapter 4 A Generalization of Bayesian Inference
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    Chapter 5 On Random Sets and Belief Functions
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    Chapter 6 Non-Additive Probabilities in the Work of Bernoulli and Lambert
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    Chapter 7 Allocations of Probability
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    Chapter 8 Computational Methods for A Mathematical Theory of Evidence
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    Chapter 9 Constructive Probability
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    Chapter 10 Belief Functions and Parametric Models
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    Chapter 11 Entropy and Specificity in a Mathematical Theory of Evidence
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    Chapter 12 A Method for Managing Evidential Reasoning in a Hierarchical Hypothesis Space
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    Chapter 13 Languages and Designs for Probability Judgment
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    Chapter 14 A Set-Theoretic View of Belief Functions
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    Chapter 15 Weights of Evidence and Internal Conflict for Support Functions
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    Chapter 16 A Framework for Evidential-Reasoning Systems
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    Chapter 17 Epistemic Logics, Probability, and the Calculus of Evidence
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    Chapter 18 Implementing Dempster’s Rule for Hierarchical Evidence
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    Chapter 19 Some Characterizations of Lower Probabilities and Other Monotone Capacities through the use of Möbius Inversion
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    Chapter 20 Axioms for Probability and Belief-Function Propagation
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    Chapter 21 Generalizing the Dempster–Shafer Theory to Fuzzy Sets
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    Chapter 22 Bayesian Updating and Belief Functions
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    Chapter 23 Belief-Function Formulas for Audit Risk
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    Chapter 24 Decision Making Under Dempster–Shafer Uncertainties
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    Chapter 25 Belief Functions: The Disjunctive Rule of Combination and the Generalized Bayesian Theorem
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    Chapter 26 Representation of Evidence by Hints
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    Chapter 27 Combining the Results of Several Neural Network Classifiers
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    Chapter 28 The Transferable Belief Model
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    Chapter 29 A k -Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory
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    Chapter 30 Logicist Statistics II: Inference
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Title
Classic Works of the Dempster-Shafer Theory of Belief Functions
Published by
Springer Science & Business Media, February 2008
DOI 10.1007/978-3-540-44792-4
ISBNs
978-3-54-025381-5, 978-3-54-044792-4
Editors

Yager, Roland R., Liu, Liping

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 5%
France 2 3%
Italy 1 2%
Singapore 1 2%
Unknown 51 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 43%
Student > Master 6 10%
Researcher 4 7%
Student > Bachelor 3 5%
Student > Postgraduate 3 5%
Other 8 14%
Unknown 9 16%
Readers by discipline Count As %
Computer Science 21 36%
Engineering 15 26%
Physics and Astronomy 2 3%
Mathematics 2 3%
Neuroscience 2 3%
Other 5 9%
Unknown 11 19%