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Unformatted text preview: 2 = σX + 4Cov(X, Y ) + 4σY = 5 − 4 + 8 = 9. Thus, P {X + 2Y ≥ 1} = P {Z ≥ 1} = P Z 3 ≥ 1 3 =Q 1 3 = 1 − Φ( 1 ) ≈ 0.3694. 3 Example 4.11.4 Let X and Y be jointly Gaussian random variables with mean zero, variance one, and Cov(X, Y ) = ρ. Find E [Y 2 |X ], the best estimator of Y 2 given X. (Hint: X and Y 2 are not jointly Gaussian. But you know the conditional distribution of Y given X = u and can use it to ﬁnd the conditional second moment of Y given X = u.) Solution: Recall the fact that E [Z 2 ] = E [Z ]2 + Var(Z ) for a random variable Z . The idea is to apply the fact to the conditional distribution of Y given X . Given X = u, the conditional distribution of Y is Gaussian with mean ρu and variance 1 − ρ2 . Thus, E [Y 2 |X = u] = (ρu)2 +1 − ρ2 . Therefore, E [Y 2 |X ] = (ρX )2 + 1 − ρ2 . Example 4.11.5 Suppose X and Y are zero-mean unit-variance jointly Gaussian random variables with correlation coeﬃcient ρ = 0.5. (a) Find Var(3X − 2Y ). (b) Find th...
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## This note was uploaded on 02/09/2014 for the course ISYE 2027 taught by Professor Zahrn during the Spring '08 term at Georgia Institute of Technology.

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