gaussian

The cutoff to even be able to ride in the Tour is far to the right on a Gaussian curve.

Sample mean and variance are sufficient statistics for independent, identically distributed samples from a univariate Gaussian distribution.

Here's a good place to start: ftp://ftp.cdc.noaa.gov/Datasets/20thC_ReanV2c/Monthlies/gaussian/monolevel/air.sfc.mon.mean.nc It's netcdf global monthly mean air temps.

We do not have Gaussian distribution on size of protected items, density of vulnerabilities, or anything else.

Often (always?) these constraints are all so very much more complex than Gaussian power analysis states.

These converge to the desired solution, as opposed to computing it directly, as with Gaussian elimination.

The other thing that prevents useful quantification is that we don't have a Gaussian distribution to be seen.

The shadows in the left are noticeably sharper and the right shadows are all non-physically blurred with a gaussian kernel or something.

For technical - picture the people interviewing as being a Gaussian curve with a normal distribution - the x-axis is how technically good you are, the y-axis is how many people reach that level.

One thing I don't like from the start in this analysis is the assumption of a Gaussian distribution for power.

Every food pretty much follows a Gaussian curve in terms of benefit.

Yeah, also I feel the rainbow colours with a Gaussian blur looks pretty dated too - reminds me of Aero Glass.

I think (although they're a little handwavey about it) that their "Gaussian blur" prior must be of this form.

In any case, there is no prior reason to exclude outliers; any large enough representative sampling of a Gaussian distribution would include "outliers".

You'll also often see the Laplacian combined with a Gaussian (the "LoG" operator) for pre-smoothing since it tends to be particularly sensitive to any noise.

The example that bothered me most was the "Gaussian processes" boomlet.

Our management structures are still highly Gaussian-biased (decades of manufacturing envy).

Gaussian elimination finds the inverse of a matrix.

The linear-quadratic-Gaussian case of dynamic programming.