Charles R Harris wrote: > Suppose you have a set of z_i and want to choose z to minimize the > average square error $ \sum_i |z_i - z|^2 $. The solution is that > $z=\mean{z_i}$ and the resulting average error is given by 2). Note that > I didn't mention Gaussians anywhere. No distribution is needed to > justify the argument, just the idea of minimizing the squared distance. > Leaving out the ^2 would yield another metric, or one could ask for a > minmax solution. It is a question of the distance function, not > probability. Anyway, that is one justification for the approach in 2) > and it is one that makes a lot of applied math simple. Whether of not a > least squares fit is useful is different question.
If you're not doing probability, then what are you using var() for? I can accept that the quantity is meaningful for your problem, but I'm not convinced it's a variance. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion