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.
--Hoyt On Mon, Apr 28, 2008 at 8:29 AM, Hoyt Koepke <[EMAIL PROTECTED]> 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. > The only way this would not be true is if: > > 1. You are not fitting the variance, but only the mean > 2. There's some numerical issue (like some of the data are represented > as integers, etc.) > > Don't know if it could be one of those issues... > > --Hoyt > > > > On Mon, Apr 28, 2008 at 7:18 AM, Alan G Isaac <[EMAIL PROTECTED]> wrote: > > 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@scipy.org > > http://projects.scipy.org/mailman/listinfo/numpy-discussion > > > > > > -- > +++++++++++++++++++++++++++++++++++ > Hoyt Koepke > UBC Department of Computer Science > http://www.cs.ubc.ca/~hoytak/ > [EMAIL PROTECTED] > +++++++++++++++++++++++++++++++++++ > -- +++++++++++++++++++++++++++++++++++ Hoyt Koepke UBC Department of Computer Science http://www.cs.ubc.ca/~hoytak/ [EMAIL PROTECTED] +++++++++++++++++++++++++++++++++++ _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
