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A symmetric random variable is called a Gaussian mixture if it has the same distribution as the product of two independent random variables, one being positive and the other a standard Gaussian random variable.
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Isoperimetric constants for product probability measures
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An application of the Fourier transform to sections of star bodies
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Learning mixtures of Gaussians
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Correlation measures
T. M. Lewis and G. Pritchard · 1999
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Learning mixtures of arbitrary Gaussians
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Majorization of sequences, sharp vector Khinchin inequalities, and bisubharmonic functions
A. Baernstein, II and R. C. Culverhouse · 2002
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Hyperplane projections of the unit ball of l p n l^{n}_{p}
F. Barthe and A. Naor · 2002
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On some inequalities for Gaussian measures
The log-Brunn-Minkowski inequality
K. J. Böröczky, E. Lutwak, D. Yang, and G. Zhang · 2012
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Khinchine type inequalities with optimal constants via ultra log-concavity
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Concentration inequalities and geometry of convex bodies
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On the best constants in the Khintchine inequality for Steinhaus variables
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The (B) conjecture for uniform measures in the plane
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A simple proof of the Gaussian correlation conjecture extended to some multivariate gamma distributions
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Wavelet thresholding for non-necessarily Gaussian noise: idealism
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Extremal sections of complex l p l_{p} -balls, 0 < p ⩽ 2 0<p\leqslant 2
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Tail properties of correlation measures
T. M. Lewis and G. Pritchard · 2003
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Solution of Shannon’s problem on the monotonicity of entropy
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The (B) conjecture for the Gaussian measure of dilates of symmetric convex sets and related problems
D. Cordero-Erausquin, M. Fradelizi, and B. Maurey · 2004
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Asymptotic geometric analysis. Part I
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Remarks on the conjectured log-Brunn-Minkowski inequality
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A reverse entropy power inequality for log-concave random vectors
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