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
Mathematics, 21.06.2019 17:40, challenggirl
Find the volume of the described solid. the solid lies between planes perpendicular to the x-axis at x = 0 and x=7. the cross sectionsperpendicular to the x-axis between these planes are squares whose bases run from the parabolay=-31x to the parabola y = 30/x. a) 441b) 147c) 864d) 882
Answers: 1
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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