Gaussian Extremality For Derivatives Of Differential Entropy Under The Additive Gaussian Noise Flow

2018 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT)(2018)

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摘要
Let Z be a standard Gaussian random variable, X be independent of Z, and t be a strictly positive scalar. For the derivatives in t of the differential entropy of X + root tZ, McKean noticed that Gaussian X achieves the extreme for the first and second derivatives, and he conjectured that this holds for general orders of derivatives. Here we show that, when the probability density function of X + root tZ is log-concave, this conjecture holds for orders up to at least five. We also recover Toscani's result on the non-negativity of the third derivative of the entropy power of X + root tZ for log-concave densities, using a much simpler argument.
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关键词
differential entropy,entropy power,Gaussian extremality,additive Gaussian noise flow,standard Gaussian random variable,probability density function,Toscani result,log-concave densities
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