Information and Divergence Measures

Dear Colleagues,

The general topic of measures can be considered as a broad scientific area aiming to develop methods and techniques for inference and modelling of various phenomena in various scientific fields.

The concept of distance is important in establishing the degree of similarity and the degree of closeness among functions, populations, or distributions. As a result, distances are related to inferential statistics, including both estimation and hypothesis testing problems, and modelling with applications in regression analysis, multivariate analysis, actuarial science, portfolio optimization, survival analysis, reliability theory, and many more. Thus, the significant importance of entropy and divergence measures that emerges in many scientific fields makes such a topic of great interest to scientists, researchers, medical experts, engineers, industrial managers, computer experts, data analysts, etc.

This issue intents to cover the recent developments in Information and Divergence Measures and presents new theoretical issues that were not previously presented in the literature, as well as the solutions of important practical problems and case studies illustrating the application methodology.

The issue is expected to be a collective work by a number of leading scientists, (data) analysts, statisticians, mathematicians, computer scientists, information theory experts, and engineers who have been working on the front end of information theory and divergence measures.

All manuscripts in the issue are going to be written by leading researchers and practitioners in their respective fields of expertise and present a plethora of innovative methods, approaches, and solutions not covered before in the literature.

Prof. Dr. Alex Karagrigoriou
Dr. Andreas Makrides
Guest Editors

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