Skip to main content

Maximum Entropy Beyond Selecting Probability Distributions

  • 1984 Accesses

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


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.


  • 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.

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-73150-6_15
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   229.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-73150-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   299.99
Price excludes VAT (USA)
Hardcover Book
USD   399.99
Price excludes VAT (USA)


  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 

Download references


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

Author information

Authors and Affiliations


Corresponding author

Correspondence to Vladik Kreinovich .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

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.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73149-0

  • Online ISBN: 978-3-319-73150-6

  • eBook Packages: EngineeringEngineering (R0)