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Using probability-density functions in the framework of evidential reasoning

  • Pascal V. Fua
Section II Approaches To Uncertainty A) Evidence Theory
Part of the Lecture Notes in Computer Science book series (LNCS, volume 286)

Abstract

To develop an approach to utilizing continuous statistical information within the Dempster-Shafer framework, we combine methods proposed by Strat and by Shafer. We first derive continuous possibility and mass functions from probability-density functions. Then we propose a rule for combining such evidence that is simpler and can be computed more efficiently than Dempster's rule. We discuss the relationship between Dempster's rule and our proposed rule for combining evidence over continuous frames.

Keywords

Mass Function Knowledge Source Combination Rule Belief Function Evidential Reasoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    G.A. Shafer, A Mathematical Theory of Evidence, pp. 237–250 (Princeton University Press, Princeton, New Jersey, 1976).Google Scholar
  2. [2]
    T.M. Strat and J.D. Lowrance, “Evidential Reasoning with Continuous Variables,” Technical Note, Artificial Intelligence Center, SRI International, Menlo Park, California (forthcoming).Google Scholar
  3. [3]
    T.M. Strat, “Continuous Belief Functions for Evidential Reasoning,” Proceedings, AAAI-84, Austin, Texas (August 1984).Google Scholar
  4. [4]
    G.A. Shafer, “Belief Functions and Possibility Measures,” University of Kansas, School of Business Working Paper No. 163 (September 1984).Google Scholar
  5. [5]
    L.A. Zadeh, “A Theory of Approximate Reasoning,” Machine Intelligence 9 (John Wiley and Sons, Inc., New York, New York, 1979).Google Scholar
  6. [6]
    G. Reynolds, D. Strahman, N. Lehrer, “Converting Feature Values to Evidence,” pp. 331–338, Proceedings of the Image Understanding Workshop, Miami, Florida (December 1985).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1987

Authors and Affiliations

  • Pascal V. Fua
    • 1
  1. 1.Artificial Intelligence CenterSRI InternationalMenlo Park

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