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 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 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

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-- +++++++++++++++++++++++++++++++++++ Hoyt Koepke UBC Department of Computer Science http://www.cs.ubc.ca/~hoytak/ hoytak@gmail.com +++++++++++++++++++++++++++++++++++

-- +++++++++++++++++++++++++++++++++++ Hoyt Koepke UBC Department of Computer Science http://www.cs.ubc.ca/~hoytak/ hoytak@gmail.com +++++++++++++++++++++++++++++++++++

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 at zero but your mean is at 50 and your standard deviation is 10, you will hardly notice the truncation. If your mean is at 14 and the standard deviation is 10, you will definitely see the truncation. That's what I understood Rich to be seeing. Cheers, Alan Isaac