Belief Functions: Theory and Applications

Proceedings of the 2nd International Conference on Belief Functions, Compiègne, France 9-11 May 2012

  • Thierry Denoeux
  • Marie-Hélène Masson

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)

Table of contents

  1. Front Matter
    Pages 1-9
  2. Hung T. Nguyen
    Pages 1-19
  3. Sawsan Kanj, Fahed Abdallah, Thierry Denœux
    Pages 21-28
  4. Jianbing Ma, Weiru Liu, Paul Miller
    Pages 29-36
  5. Anne-Laure Jousselme, Patrick Maupin
    Pages 45-52
  6. Anthony Fiche, Arnaud Martin, Jean-Christophe Cexus, Ali Khenchaf
    Pages 53-60
  7. Emmanuel Ramasso, Michèle Rombaut, Noureddine Zerhouni
    Pages 61-68
  8. Stefen Chan Wai Tim, Michèle Rombaut, Denis Pellerin
    Pages 69-76
  9. Nicolas Sutton-Charani, Sébastien Destercke, Thierry Denœux
    Pages 77-84
  10. Fabio Cuzzolin
    Pages 125-133
  11. Christophe Osswald
    Pages 135-143
  12. Thomas Burger, Sébastien Destercke
    Pages 145-152
  13. Sébastien Destercke, Thomas Burger
    Pages 153-160

About these proceedings


The theory of belief functions, also known as evidence theory or Dempster-Shafer theory, was first introduced by Arthur P. Dempster in the context of statistical inference, and was later developed by Glenn Shafer as a general framework for modeling epistemic uncertainty. These early contributions have been the starting points of many important developments, including the Transferable Belief Model and the Theory of Hints. The theory of belief functions is now well established as a general framework for reasoning with uncertainty, and has well understood connections to other frameworks such as probability, possibility and imprecise probability theories.


This volume contains the proceedings of the 2nd International Conference on Belief Functions that was held in Compiègne, France on 9-11 May 2012. It gathers 51 contributions describing recent developments both on theoretical issues (including approximation methods, combination rules, continuous belief functions, graphical models and independence concepts) and applications in various areas including classification, image processing, statistics and intelligent vehicles.




Belief Functions Intelligent Computing

Editors and affiliations

  • Thierry Denoeux
    • 1
  • Marie-Hélène Masson
    • 2
  1. 1., Centre de Recherches de RoyallieuUniversité de Technologie de CompiègneCompiègneFrance
  2. 2.Université de Picardie Jules VerneCompiègneFrance

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-29460-0
  • Online ISBN 978-3-642-29461-7
  • Series Print ISSN 1867-5662
  • Series Online ISSN 1867-5670
  • About this book
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