Random generation of mass functions: A short howto

Advances in Intelligent and Soft Computing

Volumn 164 AISC, Issue , 2012, Pages 145-152

  Burger, Thomas ^a   Destercke, Sébastien ^b  

^a CEA GRENOBLE   (France)

^b CNRS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DEMPSTER-SHAFER THEORY; MASS FUNCTIONS; RANDOM GENERATION; STATISTICAL LAWS; STATISTICAL PROPERTIES;

ALGORITHMS;

UNCERTAINTY ANALYSIS;

EID: 84861007878     PISSN: 18675662     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-29461-7_17     Document Type: Conference Paper

References (8)

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2. Smets, P., Kennes, R.: The transferable belief model. Artificial Intelligence 66, 191-234 (1994)
3. Klir, G.J.: Uncertainty and Information. John Wiley & Sons, Inc., Hoboken (2005)
4. Jousselme, A.-L., Maupin, P.: Distances in evidence theory: Comprehensive survey and generalizations. International Journal of Approximate Reasoning (2011) (accepted manuscript) (in press)
5. Shafer, G., Shenoy, P.P.: Local computation on hypertrees. Working paper No. 201, School of Business, University of Kansas (1988)
6. Bauer, M.: Approximation algorithms and decision making in the dempster-shafer theory of evidence - an empirical study. Int. J. Approx. Reasoning 17(2-3), 217-237 (1997)
7. Jousselme, A.-L., Maupin, P.: On some properties of distances in evidence theory. In: Proceedings of the Workshop on Theory of Belief Functions (2010)
8. Cuzzolin, F.: A geometric approach to the theory of evidence. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 38(4), 522-534 (2008)