When X and Y have expected values X = Y = 0, Theorem 12.3 says
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When X and Y have expected values µX = µY = 0, Theorem 12.3 says that XL(Y) = ρX,Y σX/σY Y.Show that this result is a special case of Theorem 12.8 when random vector Y is the one – dimensional random variable Y.
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From Theorem 126 we know that the minimum mean square error ...View the full answer
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Related Book For 
Probability and Stochastic Processes A Friendly Introduction for Electrical and Computer Engineers
ISBN: 978-1118324561
3rd edition
Authors: Roy D. Yates, David J. Goodman
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