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International Econometric Conference of Vietnam

ECONVN 2018: Econometrics for Financial Applications pp 186–195Cite as

Maximum Entropy Beyond Selecting Probability Distributions

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Part of the Studies in Computational Intelligence book series (SCI,volume 760)

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.

Keywords

  • Maximum Entropy Technique
  • Usual Engineering Practice
  • Small Independent Effect
  • Expert Estimates
  • Constraint Optimization Problem

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

  1. 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

    Google Scholar 

  2. 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

    Google Scholar 

  3. 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)

    CrossRef  MATH  Google Scholar 

  4. Jaynes, E.T., Bretthorst, G.L.: Probability Theory: The Logic of Science. Cambridge University Press, Cambridge (2003)

    CrossRef  Google Scholar 

  5. Nguyen, H.T., Kreinovich, V., Wu, B., Xiang, G.: Computing Statistics under Interval and Fuzzy Uncertainty. Springer, Heidelberg (2012)

    CrossRef  MATH  Google Scholar 

  6. Rabinovich, S.G.: Measurement Errors and Uncertainty: Theory and Practice. Springer, Berlin (2005)

    MATH  Google Scholar 

  7. Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. Chapman and Hall/CRC, Boca Raton (2011)

    MATH  Google Scholar 

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Acknowledgments

This work was supported in part by the National Science Foundation grant HRD-1242122 (Cyber-ShARE Center of Excellence).

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Authors and Affiliations

  1. Banking University of Ho Chi Minh City, 56 Hoang Dieu 2, Quan Thu Duc, Ho Chi Minh City, Vietnam

    Thach N. Nguyen

  2. University of Texas at El Paso, 500 W. University, El Paso, TX, 79968, USA

    Olga Kosheleva & Vladik Kreinovich

Authors
  1. Thach N. Nguyen
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  2. Olga Kosheleva
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  3. Vladik Kreinovich
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Corresponding author

Correspondence to Vladik Kreinovich .

Editor information

Editors and Affiliations

  1. Banking University HCMC, Ho Chi Minh City, Vietnam

    Ly H. Anh

  2. Banking University HCMC, Ho Chi Minh City, Vietnam

    Le Si Dong

  3. Computer Science Department, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  4. Banking University HCMC, Ho Chi Minh City, Vietnam

    Nguyen Ngoc Thach

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73149-0

  • Online ISBN: 978-3-319-73150-6

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