The concept of distance is important for establishing the degree of similarity and/or closeness between functions, populations, or distributions. As a result, distances are related to inferential statistics, including problems related to both estimation and hypothesis testing, as well as modelling with applications in regression analysis, multivariate analysis, actuarial science, portfolio optimization, survival analysis, reliability theory, and many other areas. Thus, entropy and divergence measures are always a central concern for scientists, researchers, medical experts, engineers, industrial managers, computer experts, data analysts, and other professionals.
This reprint focuses on recent developments in information and divergence measures and presents new theoretical issues as well as solutions to important practical problems and case studies illustrating the great applicability of these innovative techniques and methods. The contributions in this reprint highlight the diversity of topics in this scientific field.
The concept of distance is important for establishing the degree of similarity and/or closeness between functions, populations, or distributions. As a result, distances are related to inferential statistics, including problems related to both estimation and hypothesis testing, as well as modelling with applications in regression analysis, multivariate analysis, actuarial science, portfolio optimization, survival analysis, reliability theory, and many other areas. Thus, entropy and divergence measures are always a central concern for scientists, researchers, medical experts, engineers, industrial managers, computer experts, data analysts, and other professionals.
This reprint focuses on recent developments in information and divergence measures and presents new theoretical issues as well as solutions to important practical problems and case studies illustrating the great applicability of these innovative techniques and methods. The contributions in this reprint highlight the diversity of topics in this scientific field.
This book presents new and original research in Statistical Information Theory, based on minimum divergence estimators and test statistics, from a theoretical and applied point of view, for different...
From the reviews: "Bioinformaticians are facing the challenge of how to handle immense amounts of raw data, [...] and render them accessible to scientists working on a wide variety of problems. [This...
The concept of measuring inconsistency in information was developed by John Grant in a 1978 paper in the context of first-order logic. For more than 20 years very little was done in this area until...
How should information be measured? That is the question posed by and dealt with in this book. The concept of information has become so pervasive that people regularly refer to the present era as the...
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