Practical implementation of possibilistic probability mass functions

Soft Computing

Volumn 7, Issue 5, 2003, Pages 304-309

  Gilbert, L ^a   de Cooman, G ^a   Kerre, E E ^a  

^a GHENT UNIVERSITY   (Belgium)

Author keywords

Fuzzy number; Fuzzy probability; Linguistic uncertainty; Lowest prevision; Possibility measure

Indexed keywords

FUZZY NUMBERS; FUZZY PROBABILITY; LINGUISTIC UNCERTAINTY; LOWEST PREVISION; POSSIBILITY MEASURE;

FUZZY SETS; LINGUISTICS;

PROBABILITY;

EID: 21744457730     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-002-0217-3     Document Type: Article

References (19)

1. Berger JO (1984) The robust Bayesian viewpoint, In: Kadane JB, (ed.), Robustness of Bayesian Analyses. Elsevier Science, Amsterdam
2. Berger JO (1985) Statistical Decision Theory and Bayesian Analysis. Springer-Verlag, New York
3. de Campos LM, Huete JF, Moral S (1994)Probability intervals: a tool for uncertain reasoning. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2: 167-196
4. de Cooman G (1998) Possibilistic previsions. In: Proceedings of IPMU'98, Vol I, pp. 2-9. Éditions EDK, Paris
5. de Cooman G (2000) Precision-imprecision equivalence in a broad class of imprecise hierarchical uncertainty models. JSPI, 175-198, vol 105, 2002
6. de Cooman G (2001) A behavioural model for vague probability assessments. In preparation
7. de Cooman G, Aeyels D (1999) Supremum preserving upper probabilities. Information Sciences, 118: 173-212
8. de Cooman G, Walley P (2002) A possibilistic hierarchical model for behaviour under uncertainty. Theory and Decision, Accepted for publication
9. de Finetti B (1974) Theory of Probability, Vol 1. John Wiley & Sons, Chichester. (English Translation of Teoria delle Probabilità.)
10. Good IJ (1980) Some history of the hierarchical Bayesian methodology. In Bernardo JM, DeGroot MH, Lindley DV, Smith AFM (eds), Bayesian Statistics, Vol 1, pp. 489-519. Valencia University Press, Valencia
11. Kerre EE (1991) Basic principles of fuzzy set theory for the representation and manipulation of imprecision and uncertainty. In: Kerre EE (ed.), Introduction to the Basic Principles of Fuzzy Set Theory and Some of Its Applications, pp. 1-158. Communication & Cognition, Ghent
12. Kerre EE, Van Schooten A (1988) A deeper look on fuzzy numbers from a theoretical as well as from a practical point of view. In: Gupta M, Yamakawa T (eds), Fuzzy Logic in Knowledge-Based Systems, Decision and Control, pp. 173-196. Elsevier Science Publishers
13. Puri ML, Ralescu D (1986) Fuzzy random variables. J Math Anal Appl, 114: 409-422
14. Steyaert H, Van Parys F, Baekeland R, Kerre EE (1995) Implementation of piecewise linear fuzzy quantities. Int J Intel Syst, 10: 1049-1059
15. Walley P (1991) Statistical Reasoning with Imprecise Probabilities. Chapman and Hall, London.
16. Walley P (1996) Measures of uncertainty in expert systems. Artificial Intelligence, 83: 1-58
17. Walley P (1997) Statistical inferences based on a secondorder possibility distribution. Int J Gene Syst, 26: 337-383.
18. Walley P, de Cooman G (2001) A behavioural model for linguistic uncertainty. Info Sci, 134: 1-37
19. Zadeh LA (1984) Fuzzy probabilities. Info Proc Manag, 20: 363-372