Classic Works of the Dempster-Shafer Theory of Belief Functions / Edition 1

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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.

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Product Details

  • ISBN-13: 9783642064784
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 11/23/2010
  • Series: Studies in Fuzziness and Soft Computing Series, #219
  • Edition description: Softcover reprint of hardcover 1st ed. 2008
  • Edition number: 1
  • Pages: 806
  • Product dimensions: 6.14 (w) x 9.21 (h) x 1.64 (d)

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.

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Sort by: Showing 1 Customer Reviews
  • Anonymous

    Posted March 5, 2010

    Best on the topic

    We used the book for a research seminar at Harvard. My professor highly regards the book and considers it the bible on the Dempster-Shafer theory of belief functions.

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