Information-Preserving Probability-Possibility Transformations:

It is now generally recognized that uncertainty can be formalized in different mathematical theories [17]. Some of the theories of uncertainty are more general than others, while some are not comparable in this respect. The theories also differ from one another in their meaningful interpretations, computational complexity, robustness, and other aspects related to their utility. According to conclusions of several comparative studies [10, 11, 23, 26], none of the theories of uncertainty is superior to its competitors under all circumstances. Each theory seems to have some advantages and some disadvantages when compared with the other theories. Furthermore, this comparison is context-dependent: each theory is suitable for utilizing some types of evidence and unsuitable for other types. The different theories of uncertainty should thus be viewed as complementary.

[1]  Eric Horvitz,et al.  A Framework for Comparing Alternative Formalisms for Plausible Reasoning , 1986, AAAI.

[2]  George J. Klir,et al.  Minimal information loss possibilistic approximations of random sets , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[3]  S. Moral,et al.  On the concept of possibility-probability consistency , 1987 .

[4]  George J. Klir,et al.  DISCORD IN POSSIBILITY THEORY , 1991 .

[5]  George J. Klir,et al.  A principle of uncertainty and information invariance , 1990 .

[6]  V. V. S. Sarma,et al.  Estimation of fuzzy memberships from histograms , 1985, Inf. Sci..

[7]  D. Dubois,et al.  Unfair coins and necessity measures: Towards a possibilistic interpretation of histograms , 1983 .

[8]  D. Dubois,et al.  On Possibility/Probability Transformations , 1993 .

[9]  George J. Klir,et al.  Fuzzy sets, uncertainty and information , 1988 .

[10]  Ronald R. Yager,et al.  Fuzzy set and possibility theory , 1984, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  George J. Klir,et al.  Uncertainty in the dempster-shafer Theory - A Critical Re-examination , 1990 .

[12]  D. Dubois,et al.  Fuzzy sets and statistical data , 1986 .

[13]  George J. Klir,et al.  A MATHEMATICAL ANALYSIS OF INFORMATION-PRESERVING TRANSFORMATIONS BETWEEN PROBABILISTIC AND POSSIBILISTIC FORMULATIONS OF UNCERTAINTY , 1992 .

[14]  H. E. Stephanou,et al.  Perspectives on imperfect information processing , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  G. Klir,et al.  On probability-possibility transformations , 1992 .

[16]  George J. Klir,et al.  A Note on the Measure of Discord , 1992, UAI.

[17]  George J. Klir,et al.  Developments in Uncertainty-Based Information , 1993, Adv. Comput..

[18]  G. Klir,et al.  PROBABILITY-POSSIBILITY TRANSFORMATIONS: A COMPARISON , 1992 .

[19]  Serafín Moral Construction of a Probability Distribution from a Fuzzy Information , 1986 .

[20]  H. Trussell,et al.  Constructing membership functions using statistical data , 1986 .

[21]  G. Klir,et al.  Fuzzy Measure Theory , 1993 .

[22]  L. Zadeh,et al.  Fuzzy sets and applications : selected papers , 1987 .

[23]  Malcolm C. Harrison,et al.  An analysis of four uncertainty calculi , 1988, IEEE Trans. Syst. Man Cybern..