Computing expectations with p-boxes: Two views of the same problem

ISIPTA 2007 - Proceedings of the 5th International Symposium on Imprecise Probability: Theories and Applications

Volumn , Issue , 2007, Pages 435-444

  Utkin, L ^a   Destercke, S ^b  

^a SAINT PETERSBURG STATE FOREST TECHNICAL UNIVERSITY   (Russian Federation)

^b INSTITUT DE RADIOPROTECTION ET DE SÛRETÉ NUCLÉAIRE IRSN   (France)

Author keywords

Linear programming; Lower upper expectation; Optimization; P boxes; Random sets

Indexed keywords

CONTINUOUS SPACES; LOWER/UPPER EXPECTATION; P-BOXES; PROBABILISTIC MODELING; RANDOM SET; TWO VIEWS;

LINEAR PROGRAMMING; OPTIMIZATION; SET THEORY;

PROBABILITY;

EID: 84883246708     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. D. Berleant and J. Zhang. Using pearson correlation to improve envelopes around the distributions of functions. Re- liable Computing, 10(2):139-161, 2004.[http://ifsc.ualr.edu/jdberleant/].
2. I. Couso, S. Moral, and P. Walley. A survey of concepts of independence for imprecise probabilities. Risk Decision and Policy, 5:165-181, 2000.
3. A. Dempster. Upper and lower probabilities induced by a multivalued mapping. Annals of Mathematical Statistics, 38:325-339, 1967.
4. S. Destercke and D. Dubois. A unified view of some representations of imprecise probabilities. Int. Conf. on Soft Methods in Probability and Statistics (SMPS), Advances in Soft Computing, pages 249-257, Bristol, 2006. Springer.
5. S. Ferson, L. Ginzburg, V. Kreinovich, D. Myers, and K. Sentz. Constructing probability boxes and dempster-shafer structures. Technical report, Sandia National Laboratories, 2003.[http://www.sandia.gov/epistemic/Reports/ SAND2002-4015.pdf].
6. T. Fetz and M. Oberguggenberger. Propagation of uncertainty through multivariate functions in the framework of sets of probability measures. Reliability Engineering and System Safety, 85:73-87, 2004.
7. E. Kriegler and H. Held. Utilizing belief functions for the estimation of future climate change. I. J. of Approximate Reasoning, 39:185-209, 2005.
8. H. Regan, S. Ferson, and D. Berleant. Equivalence of methods for uncertainty propagation of real-valued random variables. I. J. of Approxi- mate Reasoning, 36:1-30, 2004.
9. P. Smets. Belief functions on real numbers. I. J. of Approximate Reasoning, 40:181-223, 2005.[http://iridia.ulb.ac.be/psmets/].
10. L. Utkin. Risk analysis under partial prior information and non-monotone utility functions. I. J. of Information Technology and Decision Making, To appear. [http://www.levvu.narod.ru/select-refern.htm].
11. P. Walley. Statistical reasoning with imprecise Probabilities. Chapman and Hall, 1991.
12. P. Walley. Measures of uncertainty in expert systems. Artifical Intelligence, 83:1-58, 1996.
13. R. Williamson and T. Downs. Probabilistic arithmetic : Numerical methods for calculating convolutions and dependency bounds. I. J. of Ap- proximate Reasoning, 4:8-158, 1990.