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An entropy-based measure for comparing distributions
June 12, 2018 @ 2:00 pm - 3:00 pm
In this talk, I will develop an entropy-based measure called case-based entropy which can be used to compare the diversity of distributions. The measure is based on computing the support of a Shannon-equivalent equi-probable distribution. It also has the capacity to compare whole or parts of distribution in a scale-free manner. I will develop the main idea from scratch and will keep the talk accessible to graduate students and researchers alike. The utility of the measure is still being explored, but one of the latest uses that I found is its use in economics as a better method to compare income or wealth inequality than the Gini index, for example. I have also used the measure to compare the diversity of complexity in a variety of distributions from the velocities of galaxies to the energy distribution of Maxwell Boltzmann, Bose-Einstein and Fermi-Dirac distributions