Information and Divergence Measures

<p>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.</p><p>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.</p>