Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem

International Journal of Approximate Reasoning

Volumn 9, Issue 1, 1993, Pages 1-35

  Smets, Philippe ^a  

^a UNIVERSITÉ LIBRE DE BRUXELLES   (Belgium)

Author keywords

Bayes' theorem; belief functions; disjunctive rule of combination

Indexed keywords

EID: 0003106837     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/0888-613X(93)90005-X     Document Type: Article

References (36)

1. Belief functions
2. The concept of distinct evidence
3. Propagating belief functions in qualitative Markov trees
4. Un modèle mathématico-statistique simulant le processus du diagnostic médical
5. Medical diagnosis: Fuzzy sets and degrees of belief
6. Bayes' theorem generalized for belief functions
7. Belief functions and parametric models
8. Informacion difusa. Relationes eentre probabilidad y possibilidad
9. A set theoretical view of belief functions
10. Representation and combination of uncertainty with belief functions and possibility measures
11. The combination of evidence in the transferable belief model
12. The transferable belief model and other interpretations of Dempster-Shafer's Model
13. Modeling and reasoning with hints
14. The nature of the unnormalized beliefs encountered in the transferable belief model
15. The dynamic of belief in the transferable belief model and specialization-generalization matrices
16. On dynamics of cautious belief and conditional objects. TR/IRIDIA/91-13
17. On the unicity of Dempster rule of combination
18. The entailment principle for Dempster-Shafer granules
19. The principle of minimum specificity as a basis for evidential reasoning
20. On the concept of possibility-probability consistency
21. Specialization a new concept for uncertainty handling with belief functions
22. Characterizing belief with minimum commitment
23. Two views of belief: Belief as generalized probability and belief as evidence
24. Fuzzy sets as a basis for a theory of possibility
25. Possibilistic inference from statistical data
26. Computational aspects of the Möbius transform
27. Reasoning with belief functions: an analysis of compatibility
28. Resolving misunderstandings about belief functions: A response to the many criticisms raised by J. Pearl