Gaussian distribution

You see here we have a normal Gaussian distribution for the animal population.

A simple Gaussian distribution is often used as an adequately accurate model.

The process at time 1 has the normal-inverse Gaussian distribution described above.

Also, the pump and probe beams used here have Gaussian distribution.

The Gaussian distribution is sometimes informally called the bell curve.

The equality holds in the case of Gaussian distributions.

In one dimension, this corresponds to a Gaussian distribution with mean 0 and variance 1.

Here is a small such data set, consisting of two points coming from two Gaussian distributions.

If the variance of the random variables is finite, a Gaussian distribution will result.

Large changes up or down are more likely than what one would calculate using a Gaussian distribution with an estimated standard deviation.

Multivariate Gaussian distribution is one of the most convenient distributions in this problem.

This weight is based on a Gaussian distribution.

This represents the tail probability of the Gaussian distribution.

Removed, because even if the test does yield a Gaussian distribution, you have to know what sort of test was used.

Construct a vector of n numbers drawn according a standard Gaussian distribution.

This causes longer-term changes to follow a Gaussian distribution.

The peak shape is usually a Gaussian distribution.

For example, in the case of a Gaussian distribution, this comprises the mean and the covariance matrix.

A Gaussian distribution is taken for the errors as:

This included pioneering work with the inverse Gaussian distribution.

It is common to work with discrete or Gaussian distributions since that simplifies calculations.

The class of normal-inverse Gaussian distributions is closed under convolution in the following sense.

This can be computed using a Gaussian Distribution function.

Gaussian distribution, parameterized by its mean and variance (so here).