w*******e
发帖数: 14 | 1 yes, the bottom line is to give the covariance matrix between X and Y
Suppose the covariance matrix is S(note s is symmetric)
you assume X~mvn(a,vx),Y~mvn(b,vy)
so Y|X=x(where x is a realization of X) is mvn with
mean b+S*inv(vx)*(x-a)
and variance vy-S*inv(vx)*S' | r****y
发帖数: 1437 | 2
sorry, forget to mention it.
Suppose y = kx + e,
where k is a constant matrix, and e is noise, also following
Gaussian Distribution.
Thanks. |
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