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Mixture distributions are extensively used as a modeling tool in diverse areas from machine learning to communications engineering to physics, and obtaining bounds on the entropy of probability distributions is of fundamental importance in many of these applications.
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The capacity of discrete-time memoryless rayleigh-fading channels
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Hyperbolic measures on infinite dimensional spaces
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Optimal concentration of information content for log-concave densities
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Arbitrarily tight bounds on differential entropy of Gaussian mixtures
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f f -divergence inequalities
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Forward and reverse entropy power inequalities in convex geometry
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