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Theorem 17.7.1 (Theorem 11.10.1: Cram r Rao inequality) The e mean-squared error of any unbiased estimator T (X) of the parameter is lower bounded by the reciprocal of the Fisher information: 1 . (17.70) var(T ) J ( ) We now prove a fundamental relationship between the differential entropy and the Fisher information: Theorem 17.7.2 (de Bruijn s identity: entropy and Fisher information) Let X be any random variable with a nite variance with a density f (x). Let Z be an independent normally distributed random variable with zero mean and unit variance. Then 1 (17.71) he (X + tZ) = J (X + tZ), t 2 where he is the differential entropy to base e. In particular, if the limit exists as t 0, 1 he (X + tZ) (17.72) = J (X). t 2 t=0 Proof: Let Yt = X + tZ. Then the density of Yt is gt (y) = Then gt (y) = t = use qr code in excel Generate QR code in Excel [SOLVED] - Excel Help Forum
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