Hello,
I'm trying to use Mclust to fit a Gaussian Mixture Model to a
mulitdimensional data set.
Because of the specific source of my data, I know that all components
have the same variance and that the covariance between dimensions is
zero (modelname=VII).
Furthermore, I have a reliable estimate of the variance of the components.
I want to to use this estimate as a prior in mclust, hoping that
exploiting this knowledge will yield better estimates of the number of
components and their means (which are the unknowns).
First I have a general question: Is this a sensible thing to do? As
far as I can see (which might be not too far), this will indeed lead
to more robust estimates. But is this true?
Another question concerns the practical side of things. How can I do
this in mclust? I've read through the manual but this leaves me
uncertain about the exact implementation (and earlier posts about this
problem seem not to have been answered by the mailing list).
If specifying a prior for the covariance matrix is possible (and
sensible) in mclust, could someone provide an example of how to
specify a prior on the covariance matrix?
Thank you,
Dieter
