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SW VandeCarr
Apr20-10, 11:27 AM
P: 2,499
Quote Quote by kasraa View Post
Part one:

The posterior [tex] p \left( x|Z \right) [/tex], has a mean and a (co)variance. Its mean is the MMSE estimator, [tex] E \left[ x|Z \right] [/tex], and its variance (or the trace of its covariance matrix, if it's a random vector) is the minimum mean squared error. Am I right?
I don't think so. For a random vector of observations, the MMSE for the posterior estimate is the minimized trace of the covariance matrix. This is consistent with the discussion in the link I provided. As for the rest, I'm not following you. I don't understand why you're double conditioning on Z for instance. Someone else will have to try and help you