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  • Book
  • © 2008

Classic Works of the Dempster-Shafer Theory of Belief Functions

  • Collects the key original contributions that are widely recognized in the field of Dempster-Shafer Theory of Belief functions

  • 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

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 219)

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  • ISBN: 978-3-540-44792-4
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Softcover Book USD 399.99
Price excludes VAT (USA)
Hardcover Book USD 399.99
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Table of contents (30 chapters)

  1. Front Matter

    Pages I-XIX
  2. A Generalization of Bayesian Inference

    • Arthur P. Dempster
    Pages 73-104
  3. On Random Sets and Belief Functions

    • Hung T. Nguyen
    Pages 105-116
  4. Allocations of Probability

    • Glenn Shafer
    Pages 183-196
  5. Constructive Probability

    • Glenn Shafer
    Pages 217-264
  6. Belief Functions and Parametric Models

    • Glenn Shafer
    Pages 265-290
  7. Languages and Designs for Probability Judgment

    • Glenn Shafer, Amos Tversky
    Pages 345-374
  8. A Set-Theoretic View of Belief Functions

    • Didier Dubois, Henri Prade
    Pages 375-410
  9. A Framework for Evidential-Reasoning Systems

    • John D. Lowrance, Thomas D. Garvey, Thomas M. Strat
    Pages 419-434
  10. Implementing Dempster’s Rule for Hierarchical Evidence

    • Glenn Shafer, Roger Logan
    Pages 449-476

About this book

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. This book will serve as 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. From over 120 nominated contributions, the editors selected 30 papers, which are widely regarded as classics and will continue to make impacts on the future development of the field. The contributions are grouped into seven sections, including conceptual foundations, theoretical perspectives, theoretical extensions, alternative interpretations, and applications to artificial intelligence, decision-making, and statistical inferences. The book also includes a foreword by Dempster and Shafer reflecting the development of the theory in the last forty years, and an introduction describing the basic elements of the theory and how each paper contributes to the field.

Keywords

  • Bayesian inference
  • Mathematica
  • artificial intelligence
  • decision making
  • decision theory
  • economics
  • expert system
  • fuzzy
  • fuzzy logic
  • fuzzy set
  • fuzzy sets
  • intelligence
  • probability
  • statistics
  • uncertainty

Editors and Affiliations

  • Machine Intelligence Institute, Iona College, New Rochelle, USA

    Roland R. Yager

  • Department of Management and Information Systems, University of Akron College of Business Administration, 351 Akron, USA

    Liping Liu

Bibliographic Information

Buying options

eBook USD 309.00
Price excludes VAT (USA)
  • ISBN: 978-3-540-44792-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 399.99
Price excludes VAT (USA)
Hardcover Book USD 399.99
Price excludes VAT (USA)