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article · Entropy · 2022

Logical Entropy of Information Sources

Peng Xu, Yamin Sayyari, Saad Ihsan Butt

Cited by 5Open access

Abstract

, logical mutual information, and the logical entropy for information sources. We found upper and lower bounds for the logical entropy of a random variable by using convex functions. We show that the logical entropy of the joint distributions X1 and X2 is always less than the sum of the logical entropy of the variables X1 and X2. We define the logical Shannon entropy and logical metric permutation entropy to an information system and examine the properties of this kind of entropy. Finally, we examine the amount of the logical metric entropy and permutation logical entropy for maps.

Cite this paper

APA 7

Xu, P., Sayyari, Y., & Butt, S. I. (2022). Logical Entropy of Information Sources. Entropy, 24(9), 1174. https://doi.org/10.3390/e24091174

MLA 9

Xu, Peng, et al. “Logical Entropy of Information Sources.” Entropy, vol. 24, no. 9, 2022, p. 1174, https://doi.org/10.3390/e24091174.

Chicago

Xu, Peng, Yamin Sayyari, and Saad Ihsan Butt. 2022. “Logical Entropy of Information Sources.” Entropy 24 (9): 1174. https://doi.org/10.3390/e24091174.

Harvard

Xu, P., Sayyari, Y. and Butt, S.I. (2022) ‘Logical Entropy of Information Sources’, Entropy, 24(9), p. 1174. Available at: https://doi.org/10.3390/e24091174.

BibTeX
@article{xu2022logical,
  title = {{Logical Entropy of Information Sources}},
  author = {Xu, Peng and Sayyari, Yamin and Butt, Saad Ihsan},
  journal = {Entropy},
  volume = {24},
  number = {9},
  pages = {1174},
  year = {2022},
  doi = {10.3390/e24091174}
}

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