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Unformatted text preview: X 2 normal distribution with mean value vector and covariance matrix: m 1 m = , and = 11 12 ( 12 = 21 T ). m 2 21 22 Then, given X 2 = x 2 , the conditional vector (X 1  X 2 = x 2 ) has jointly normal distributions with parameters: 1 m 12 (x 2 ) = m 1 + 12 (x m 2 ) 22 2 (2) T 12 (x ) = 11 12 1 2 22 12 Notice again that 12 does not depend on x 2 . As for the scalar case, Eq. 2 may be used in approximation when X does not have multivariate normal distribution or when the distribution of X is not known, except for the mean vector m and covariance matrix ....
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This note was uploaded on 02/27/2008 for the course PTE 461 taught by Professor Donhill during the Fall '07 term at USC.
 Fall '07
 DonHill

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