On Mon, 28 Apr 2008, Hoyt Koepke wrote:
> I may not understand what you are asking, Rich, but I'm
> not sure I agree with Alan. A Gaussian fit to data
> x should fit exactly as well as data fit to ax, a > 0,
> just with a variance a^2 times the original
My point was different.
If you truncate
On Mon, 28 Apr 2008, Hoyt Koepke wrote:
> A Gaussian fit to data x should fit exactly as well as data fit to ax, a >
> 0, just with a variance a^2 times the original. The only way this would
> not be true is if:
Hoyt,
This is what I expected, too.
> 1. You are not fitting the variance, but o
Wait, I think I see what Alan is saying. When you use a gaussian
approximation on truncated data, the accuracy of the truncation is
very dependent on where in the interval the mean is. If it's near the
edges, the results will be worse. The width of the interval, though,
is a separate factor.
--
I may not understand what you are asking, Rich, but I'm not sure I
agree with Alan. A Gaussian fit to data x should fit exactly as well
as data fit to ax, a > 0, just with a variance a^2 times the original.
The only way this would not be true is if:
1. You are not fitting the variance, but only
Hi Rich,
If your data is truncated at zero, it is not Gaussian (drawn
from a normal). You will notice this when you shrink the
range of values (unless the variance is tiny).
Cheers,
Alan Isaac
___
Numpy-discussion mailing list
Numpy-discussion@sci
