Joint Treatment of Imprecision and Randomness in Uncertainty Propagation

Modern Information Processing

Volumn , Issue , 2006, Pages 37-47

  Baudrit, C ^a   Dubois, D ^a   Guyonnet, D ^b   Fargier, H ^a  

^a IRIT   (France)

^b BRGM   (France)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 34247233727     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1016/B978-044452075-3/50004-2     Document Type: Chapter

References (25)

1. Baudrit C., Dubois D. Comparing Methods for Joint Objective and Subjective Uncertainty Propagation with an example in a risk assessment. Accepted to Fourth International Symposium on Imprecise Probabilities and Their Applications (ISIPTA'05) 2005.
2. Baudrit, C., Guyonnet, D., Dubois, D. Post-processing the hybrid method for addressing uncertainty in risk assessments. technical note accepted to Journal of Environmental Engineering.
3. Baudrit C., Dubois D., Fargier H. Practical representation of incomplete probabilistic information. Advances in Soft Computing: Soft Methods of Probability and Statistics conference, Oviedo 2004, 149-156. Springer.
4. Baudrit C., Dubois D., Fargier H. Propagation of Uncertainty involving Imprecision and Randomness. THIRD EUSFLAT'03, Zittau 2003, 653-658.
5. Berleant D., Goodman-Strauss C. Bounding results of Arithmetic Operations on Random Variables of Unknown Dependencies using Interval Arthmetic. Reliable Computation 1998, 4:147-165.
6. Couso I., Moral S., Walley P. A survey of concepts of independence for imprecise probabilities. Risk Decision and Policy 2000, 5:165-180.
7. Connover W.J., Iman R.L. A Distribution-Free Approach to Inducing Rank Correlation Among Input Variables. Technometric 1982, 3:311-334.
8. Dubois D., Kerre E., Mesiar R., Prade H. Fuzzy interval analysis. Fundamentals of Fuzzy Sets 2000, 483-581. Kluwer, Boston, Mass. D. Dubois, H. Prade (Eds.).
9. Dubois D., Prade H. Random sets and Fuzzy Interval Analysis. Fuzzy Sets and Systems 1991, 42:87-101.
10. Dubois D., Prade H. The mean value of a fuzzy number. Fuzzy Sets & Systems 1987, 24:279-300.
11. Ferson, S., Ginzburg, L., Akcakaya, R. Whereof one cannot speak: when input distributions are unknown. To appear in Risk Analysis.
12. Ferson S. What Monte Carlo methods cannot do. Human and Ecology Risk Assessment 1996, 2:990-1007.
13. Ferson S., Ginzburg L.R. Different methods are needed to propagate ignorance and variability. Reliability Engineering and Systems Safety 1996, 54:133-144.
14. Ferson S., Ginzburg L.R. Hybrid Arithmetic. Proceedings of ISUMA-NAFIPS'95 1995, 619-623. IEEE Computer Society Press, Los Alamitos, California.
15. Burgman M.A., Ferson S. Correlation, dependency bounds and extinction risks. Biological Conservation 1995, 73:101-105.
16. Ferson S., Long T.F. Conservative uncertainty propagation in environmental risk assessments. ASTM STP 1994, 1218:97-110. Am. Soc. for Testing and Materials, Philadelphia. J.S. Hughes (Ed.).
17. Ferson S. Using fuzzy arthmetic in Monte Carlo simulation of fishery populations. Proceedings of the ISMSEFP, Anchorage 1992, 595-608. T. Quinn (Ed.).
18. Guyonnet D., Bourgine B., Dubois D., Fargier H., Côme B., Chilès J.P. Hybrid approach for addressing uncertainty in risk assessments. Journal of Environmental Engineering 2003, 129:68-78.
19. Kaufmann A., Gupta M.M. Introduction to Fuzzy Arithmetic: Theory and Applications 1985, Van Nostrand Reinhold, New York.
20. Kruse R., Meyer K.D. Statistics with Vague Data 1987, Reidel, Dordrecht, Netherlands.
21. Nelsen R.B. An Introduction to Copulas. Lecture Notes in Statistics 1999, 139. Springer-Verlag, New York.
22. Shafer G.A. Mathematical Theory of Evidence 1976, Princeton University Press.
23. Yager R.R. A procedure for ordering fuzzy subsets of the unit interval. Information science 1981, 24:143-161.
24. Baudrit, C., Guyonnet, D., Dubois, D. Post-processing the hybrid method for addressing uncertainty in risk assessments. technical note accepted to Journal of Environmental Engineering.
25. Ferson, S., Ginzburg, L., Akcakaya, R. Whereof one cannot speak: when input distributions are unknown. To appear in Risk Analysis.