• Mukesh Kumar Sharma
• Dr. Alok Darshan Kothiyal

A mathematical communication theory in the context of communication theory. It develops a way to research disciplines such as coding theory, semantics, decision theory, economics, radar detection, biology, psychology, and many others that demonstrate the necessity and importance of: information theory. Shannon proposed a probabilistic phenomenon to define communication through the concept of entropy, while at the same time creating the concepts of entropy and mutual information. Entropy is used to measure the uncertainty of a random variable associated with a randomized experiment. On the other hand, the mutual information quantifies the dependency between two random variables. Information theory examines all the theoretical problems associated with the transmission of information via communication channels. This includes studying the measurement of uncertainty (information) and various practical and economical methods for encoding information for transmission. An important feature of Shannon's information theory is that the term information can often have a mathematical meaning as a numerically measurable quantity, based on a probabilistic model, so that solutions to many important memory problems and the transmission of information can be in the form of this measure of information be formulated.

Information Measures, Shannon's entropy, Leibler relative-entropy

"A STUDY OF GENERALIZED INFORMATION MEASURES & THEIR INEQUALITES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.228-235, December-2018, Available :http://www.jetir.org/papers/JETIR1812B31.pdf

"A STUDY OF GENERALIZED INFORMATION MEASURES & THEIR INEQUALITES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp228-235, December-2018, Available at : http://www.jetir.org/papers/JETIR1812B31.pdf