I think that Dmitri overstates his case a bit.
This multiplication in observation space works for some algorithms, not for
others. Ordinary least squares regression is somewhat of an exception
here. Logistic regression is a simple counterexample.
It is still useful to have a vector weight and it helps users. It may be
useful in some situations to also all a full correlation matrix, but I
haven't had a need for that yet.
On Sun, Feb 22, 2009 at 11:24 AM, Dimitri Pourbaix <pourbaix@astro.ulb.ac.be
> wrote:
> Either one considers the full weighting matrix (including potential
> correlation between observations) or one does not account for any weight
> at all. By premultiplying both the function matrix and the observation
> vector by the square root of the weight matrix, one can forget about it
> completely in the rest of the computation.
>

Ted Dunning, CTO
DeepDyve
