Hi, 1- Please explain conditional & unconditional mean square error, and their difference. 2- Which one is the solution for minimum MSE estimation? (that is conditional expectation: [tex] E \left[ X|Y \right] [/tex]. I meant which one is minimized by selecting the conditional expectation.) 3- What is the relation between these two and covariance matrix in Kalman Filter? IMO, the trace of Kalman's covariance (error covariance matrix) is one of these MSEs, but I don't know which one. 4- Is there any other interpretation of Kalman's covariance matrix than the one I mentioned above? (of course there is. I meant I don't know any other and please help me Thanks a lot.