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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

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References

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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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Correspondence to Vladik Kreinovich .

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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

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