Abstract
In this chapter, all the compound notions of simple, joint, conditional, and mutual logical entropy are defined and then the corresponding notions of Shannon entropy are derived via the dit-bit transform. Moreover, a number of other notions of divergence, cross entropy, and Hamming distance are developed for logical entropy along with the corresponding notions for Shannon entropy. And finally, a number of intriguing parallels between the two entropies and related inequalities are developed which allow some inequalities directly relating the two entropies.
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Notes
- 1.
This is investigated in Rossi [6].
References
Cover, Thomas, and Joy Thomas. 1991. Elements of Information Theory. New York: John Wiley.
Csiszar, Imre, and Janos Körner. 1981. Information Theory: Coding Theorems for Discrete Memoryless Systems. New York: Academic Press.
Kullback, Solomon, and Richard A. Leibler. 1951. On information and sufficiency. Annals of Mathematical Statistics 22: 79–86. https://doi.org/10.1214/aoms/1177729694.
McEliece, Robert J. 1977. The Theory of Information and Coding: A Mathematical Framework for Communication (Encyclopedia of Mathematics and its Applications, Vol. 3). Reading MA: Addison-Wesley.
Rao, C. R. 1982. Diversity and Dissimilarity Coefficients: A Unified Approach. Theoretical Population Biology. 21: 24–43.
Rossi, Giovanni. 2011. Partition Distances. arXiv:1106.4579v1.
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Ellerman, D. (2021). The Compound Notions for Logical and Shannon Entropies. In: New Foundations for Information Theory. SpringerBriefs in Philosophy. Springer, Cham. https://doi.org/10.1007/978-3-030-86552-8_3
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DOI: https://doi.org/10.1007/978-3-030-86552-8_3
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