A question about conditional mean

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    Conditional Mean
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The discussion focuses on calculating the conditional expectation E(X|X_{sub}=A) for a multivariate normal distribution X ~ N(mu, Sigma). The key formula provided is E(Y|X=x) = mu_Y + Sigma_{YX} Sigma^{-1}_X (x - mu_x), which is essential for determining the conditional distribution. Participants acknowledge that this topic is well-documented in standard statistics textbooks. The initial confusion about the concept is addressed, highlighting the importance of understanding conditional distributions in multivariate settings. Overall, the conversation emphasizes the significance of recognizing established statistical principles.
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X (n by 1) follows a multivariate normal distribution, i.e.,

X ~ N(mu, Sigma). mu is n by 1, Sigma is n by n.

What is

E(X|X_{sub}=A)?

where the index 'sub' (m by 1) is a subset of {1,2,..,n}, A is m by 1, 1 <= m < n.
 
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Thanks a lot for the reference! This is the right answer. Actually, this is about the conditional distribution of multivariate normal variables, and well known in any standard textbook of basic statistics. However, I failed to realize this early. :(
 
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