Abstract
Computing statistics is important. Traditional data processing in science and engineering starts with computing the basic statistical characteristics such as the population mean and population variance:
Keywords
- Practical Situation
- Interval Uncertainty
- Term Versus
- Computing Versus
- Fuzzy Interval
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Dantsin, E., Kreinovich, V., Wolpert, A., Xiang, G.: Population Variance under Interval Uncertainty: A New Algorithm. Reliable Computing 12(4), 273–280 (2006)
Dubois, D., Fargier, H., Fortin, J.: The empirical variance of a set of fuzzy intervals. In: Proceedings of the 2005 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE’2005, Reno, Nevada, May 22–25, 2005, pp. 885–890 (2005)
Klir, G., Yuan, B.: Fuzzy sets and fuzzy logic: theory and applications. Prentice Hall, Upper Saddle River (1995)
Kreinovich, V., Longpré, L., Starks, S.A., Xiang, G., Beck, J., Kandathi, R., Nayak, A., Ferson, S., Hajagos, J.: Interval Versions of Statistical Techniques, with Applications to Environmental Analysis, Bioinformatics, and Privacy in Statistical Databases. Journal of Computational and Applied Mathematics 199(2), 418–423 (2007)
Kreinovich, V., Xiang, G., Starks, S.A., Longpre, L., Ceberio, M., Araiza, R., Beck, J., Kandathi, R., Nayak, A., Torres, R., Hajagos, J.: Towards combining probabilistic and interval uncertainty in engineering calculations: algorithms for computing statistics under interval uncertainty, and their computational complexity. Reliable Computing 12(6), 471–501 (2006)
Nguyen, H.T., Walker, E.A.: A first course in fuzzy logic. CRC Press, Boca Raton (2005)
Rabinovich, S.: Measurement Errors and Uncertainties: Theory and Practice. Springer, New York (2005)
Vavasis, S.A.: Nonlinear Optimization: Complexity Issues. Oxford University Press, New York (1991)
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Xiang, G., Kreinovich, V. (2007). Estimating Variance Under Interval and Fuzzy Uncertainty: Case of Hierarchical Estimation. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds) Foundations of Fuzzy Logic and Soft Computing. IFSA 2007. Lecture Notes in Computer Science(), vol 4529. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72950-1_1
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DOI: https://doi.org/10.1007/978-3-540-72950-1_1
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