Abstract
Traditionally, the Maximum Entropy technique is used to select a probability distribution in situations when several different probability distributions are consistent with our knowledge. In this paper, we show that this technique can be extended beyond selecting probability distributions, to explain facts, numerical values, and even types of functional dependence.
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References
Garibaldi, J.: Type-2 Beyond the Centroid. In: Proceedings of the IEEE International Confreence on Fuzzy Systems FUZZ-IEEE 2017, Naples, Italy, 8–12 July 2017
Hobbs, J.R.: Half orders of magnitude. In: Obrst, L., Mani, I. (eds.) Proceeding of the Workshop on Semantic Approximation, Granularity, and Vagueness, A Workshop of the Seventh International Conference on Principles of Knowledge Representation and Reasoning KR 2000, Breckenridge, Colorado, pp. 28–38, 11 April 2000
Hobbs, J., Kreinovich, V.: Optimal choice of granularity in commonsense estimation: why half-orders of magnitude. Int. J. Intell. Syst. 21(8), 843–855 (2006)
Jaynes, E.T., Bretthorst, G.L.: Probability Theory: The Logic of Science. Cambridge University Press, Cambridge (2003)
Nguyen, H.T., Kreinovich, V., Wu, B., Xiang, G.: Computing Statistics under Interval and Fuzzy Uncertainty. Springer, Heidelberg (2012)
Rabinovich, S.G.: Measurement Errors and Uncertainty: Theory and Practice. Springer, Berlin (2005)
Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. Chapman and Hall/CRC, Boca Raton (2011)
Acknowledgments
This work was supported in part by the National Science Foundation grant HRD-1242122 (Cyber-ShARE Center of Excellence).
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Nguyen, T.N., Kosheleva, O., Kreinovich, V. (2018). Maximum Entropy Beyond Selecting Probability Distributions. In: Anh, L., Dong, L., Kreinovich, V., Thach, N. (eds) Econometrics for Financial Applications. ECONVN 2018. Studies in Computational Intelligence, vol 760. Springer, Cham. https://doi.org/10.1007/978-3-319-73150-6_15
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DOI: https://doi.org/10.1007/978-3-319-73150-6_15
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