"Feng Zhang" <[EMAIL PROTECTED]> writes:
> There is no way to answer this question?
>
> even for writing the sample covariance matrix
> formulation for the data set [X, Y] where
> X(n observations) and Y (m observations) are from
> the class 1 and class 2 which both are
> multidimensional normal distribution?
It's hardly an R question, is it? However, the calculation is easy
enough in principle, at least in your original formulation: You need
the conditioning event C (the latent class) and then work out
E(X) = E(E(X|C)) = a1 * mu1 + a2 * mu2 = mu
V(X) = E(V(X|C)) + V(E(X|C)) = a1*V1 + a2*V2 + foo
with
foo = a1 * (mu1 - mu)^2 + a2 * (mu2 - mu)^2 = a1 * a2 * (mu1 - mu2)^2
where "x^2" really means x x' (==outer(x,x)) so that you get a matrix
of the proper dimensions. (And do check the math rather than believe
that I get it right 1st time...)
-p
>
> ----- Original Message -----
> From: "Feng Zhang" <[EMAIL PROTECTED]>
> To: "R-Help" <[EMAIL PROTECTED]>
> Sent: Tuesday, February 11, 2003 12:11 AM
> Subject: [R] Covariance matrix for GMM
>
>
> > Hey, All
> >
> > Now I generate a data vector X (d-dimension column vector) from a Gaussian
> > Mixture Model (GMM).
> > That is, the pdf of vector X is
> > f(X) = a1*N(u1, Cov1) + a2*(u2, Cov2)
> > where a1+a2 = 1, N is multidimensional normal distribution, ui is the mean
> > vecotr, Covi is the covariance matrix, i=1, 2.
> >
> > So can I get the close forms of the mean and covariance matrix for the
> > random vector X?
> >
> > Thanks very much.
> >
> > Fred
> >
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--
O__ ---- Peter Dalgaard Blegdamsvej 3
c/ /'_ --- Dept. of Biostatistics 2200 Cph. N
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907
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