A linear regression model with Gaussian noise is used to compute .

The Gaussian noise prior has mean zero, and variance which follows .

The test is repeated using a different set target faces that have different levels of Gaussian noise.

However, these cables had other impairments besides Gaussian noise, preventing such rates from becoming practical in the field.

If the noise also has a normal distribution, it is called normal or Gaussian white noise.

They are also used in systems theory in connection with nonlinear operations on Gaussian noise.

In Gaussian noise, each pixel in the image will be changed from its original value by a (usually) small amount.

In particular, if each sample has a normal distribution with zero mean, the signal is said to be Gaussian white noise.

A special case which is easy to calculate k(t) is white gaussian noise.

Gaussian noise with standard deviation of 0.1 is then added to each variable.