Edgeworth Theoretic Characterization of Mutual 
Information Underneath Joint and Conditional 
Probability Distribution in Algorithmic Information 
Theory

Rohit Kumar Verma; JharanaChandrakar

doi:https://doi.org/10.14445/22315373/IJMTT-V70I6P101

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 70 | Issue 6 | Year 2024 | Article Id. IJMTT-V70I6P101 | DOI : https://doi.org/10.14445/22315373/IJMTT-V70I6P101

Edgeworth Theoretic Characterization of Mutual Information Underneath Joint and Conditional Probability Distribution in Algorithmic Information Theory

Rohit Kumar Verma, JharanaChandrakar

Received	Revised	Accepted	Published
06 Apr 2024	20 May 2024	09 Jun 2024	29 Jun 2024

Abstract

The science of communication started to attract a lot of attention in the early 1900s. Communication and the transmission of information was rapidly becoming a global phenomenon due to the ongoing growth of telegraph communication, the recent invention of the telephone [3], and, most importantly, the necessity for quick and dependable connections between military groups during the World War. There was an urgent need by the 1940s for a mathematical theory that would direct the development of more advanced communication technologies.

Keywords

Entropy, Information, Information improvements, Discrete information, Continuous information. 1. Introduction The seminal work that gave rise to this theory was the groundbreaking publication A Mathematical Theory o

References

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Citation :

Rohit Kumar Verma, JharanaChandrakar, "Edgeworth Theoretic Characterization of Mutual Information Underneath Joint and Conditional Probability Distribution in Algorithmic Information Theory," International Journal of Mathematics Trends and Technology (IJMTT), vol. 70, no. 6, pp. 1-12, 2024. Crossref, https://doi.org/10.14445/22315373/IJMTT-V70I6P101