[R] Covariance matrix for GMM

[Peter Dalgaard BSA]

"Feng Zhang" <f0z6305 at labs.tamu.edu> 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" <f0z6305 at labs.tamu.edu>
> To: "R-Help" <r-help at stat.math.ethz.ch>
> 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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> > http://www.stat.math.ethz.ch/mailman/listinfo/r-help
> 
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-- 
   O__  ---- Peter Dalgaard             Blegdamsvej 3  
  c/ /'_ --- Dept. of Biostatistics     2200 Cph. N   
 (*) \(*) -- University of Copenhagen   Denmark      Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk)             FAX: (+45) 35327907