Mathematics, 06.03.2020 00:50 gthif6088
Recall the covariance of two random variables X and Y is defined as Cov(X, Y) = E[(X − E[X])(Y − E[Y])]. For a multivariate random variable Z (i. e., each index of Z is a random variable), we define the covariance matrix Σ such that Σi j = Cov(Zi , Zj). Concisely, Σ = E[(Z − µ)(Z − µ) > ], where µ is the mean value of the random column vector Z. Prove that the covariance matrix is always positive semidefinite (PSD).
Answers: 2
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Recall the covariance of two random variables X and Y is defined as Cov(X, Y) = E[(X − E[X])(Y − E[Y...
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