This preview shows pages 1–2. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: Let X = X 1 , where X 1 and X 2 are subvectors of X. Suppose X has multivariate X 2 normal distribution with mean value vector and covariance matrix: m = m 1 , 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: m 12 (x 2 ) = m 1 + 12 22 1 (x 2 m 2 ) (2) 1 T 12 (x 2 ) = 11 12 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 ....
View Full
Document
 Spring '05
 GeorgeKocur

Click to edit the document details