The principle of possibility maximum specificity as a basis for measurement uncertainty expression

2009 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement, AMUEM 2009

Volumn , Issue , 2009, Pages 5-9

  Mauris, Gilles ^a  

^a Université de Savoie   (France)

Author keywords

Coverage intervals; Maximum entropy principle; Maximum specificity principle; Measurement uncertainty; Possibility theory

Indexed keywords

COVERAGE INTERVALS; MAXIMUM ENTROPY PRINCIPLE; MAXIMUM SPECIFICITY PRINCIPLE; MEASUREMENT UNCERTAINTY; POSSIBILITY THEORY;

ENTROPY; FUZZY LOGIC; MAXIMUM PRINCIPLE; MEASUREMENTS; PROBABILITY DISTRIBUTIONS; STRUCTURES (BUILT OBJECTS);

UNCERTAINTY ANALYSIS;

EID: 70449440616     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AMUEM.2009.5207599     Document Type: Conference Paper

References (19)

1. Cox M.G. and P.M. Harris, Software support for metrology best practice guide no. 6: Uncertainty evaluation, NPL Report, 2006.
2. Guide for the expression of uncertainty in measurement, ISO, second edition, 1995.
3. GUM Supplement 1 Propagation of probability distributions using a Monte Carlo method, JCGM, 2008, 90 pages.
4. Jaynes E.T., "Prior probabilities", IEEE Trans. On System Science and Cybernetics, Vol. 4, 1968, pp. 227-241.
5. D. Dubois and H. Prade, When upper probabilities are possibility measures, Fuzzy Sets and Systems 49, 1992, pp. 65-74.
6. Mauris G., Lasserre V., Foulloy L., "Fuzzy modeling of measurement data acquired from physical sensors", IEEE Trans. on Measurement and Instrumentation, Vol. 49, No. 6, December 2000, pp. 1201-1205
7. Mauris G., Lasserre V., Foulloy L., "A fuzzy approach for the expression of uncertainty in measurement", Int. Journal of Measurement, Vol. 29, No. 3, March 2001, pp. 165-177.
8. Ferrero A., Salicone S., "The random-fuzzy variables: a new approach to the expression of uncertainty", IEEE Trans. On Instrumentation and Measurement, Vol. 53, No. 5, 2004, pp. 1370-1377.
9. Uffink J., "Can the maximum entropy principle be explained as a consistency requirement?", Studies in History and Philisophy of Modern Physics Vol. 26, No. 3, 1995, pp. 223-261.
10. Jaynes E.T., "Information theory and statistical mechanics", Physical Review, Vol. 106, 1957, pp. 620-630.
11. W. Woeger, "Probability assignment to systematic deviations by the principle of maximum entropy", IEEE Trans. On Instrumentation and Measurement, Vol. 36, No. 2, 1987, pp. 655-658.
12. R. Cordero, P. Roth, "Assigning probability density functions in context of information shortage", Metrologia, Vol. 41, 2004, pp. 122-125.
13. G. Iuculano, L. Nielsen, A. Zanobini, G. Pellegrini, "The principle of maximum entropy applied in the evaluation of the measurement uncertainty", IEEE Trans. On Instrumentation and Measurement, Vol; 56, No. 3, 2007 pp. 717-722.
14. Zadeh L.A., "Fuzzy sets as a basis for a theory of possibility", Fuzzy Sets and Systems, Vol. 1, No. 1, 1978, pp. 3-28.
15. Dubois D., Foulloy L., Mauris G., Prade H., "Probability-possibility transformations, triangular fuzzy sets and probabilistic inequalities", Reliable Computing, Vol. 10, No. 4, 2004, pp. 273-297.
16. Mauris G., "Fuzzy representation of incomplete knowledge about infinite support probability distributions in a measurement context," IEEE International Conference on Fuzzy Systems FUZZ-IEEE 2007, CD-ROM, London, UK, July 2007, 6 pages.
17. Mauris G., "A comparison of possibility and probability approaches for modelling poor knowledge on measurement distribution", IEEE Instrumentation and Measurement Technology Conference IMTC'07, CD-ROM, Varsaw, Poland, May 2007, 5 pages
18. Mauris G., Representing and approximating symmetric and asymmetric probability coverage intervals by possibility distributions, IEEE Trans. on Instrumentation and Measurement, Vol. 58, No. 1, 2009, pp. 41-45.
19. Brockett P.L., Charnes A., Paick K., "Information theoretic approach to unimodal density estimation", IEEE Trans. on Information Theory, Vol. 41, No. 3, May 1995, pp. 824-829.