There are many places where one can get a Gaussian Copula. One can get a Gaussian Copula at popular on the web sources such as Wired, UCL Finds, and SPS.
The Gaussian Copula function for finance has been totally discredited and you shouldn't touch it with a barge-pole. See The Formula That Sank Wall Street in Wired magazine.
A zero copula is the joining of a subject to a predicate without the use of a copula, such as "the more the merrier".
the gaussian filter is also known as Gaussian smoothing and is the result of blurring an image by a Gaussian function.
The Gaussian probability density distribution (pdf) is referred to as the Normal distribution. The Gaussian model results in a Gaussian pdf. Interesting, it didn't come from Gauss, but de Moivre, one of the greatest mathematicians of the 18th century, at least in my opinion. See related links.
The verb "to be" is called the "copula". It is also one of the "linking verbs" in English.
The Gaussian distribution is the same as the normal distribution. Sometimes, "Gaussian" is used as in "Gaussian noise" and "Gaussian process." See related links, Interesting that Gauss did not first derive this distribution. That honor goes to de Moivre in 1773.
Gaussian distribution can be studied at many online sources such as Kahn Academy, or by consulting a professor, or teacher specializing in statistics.
autocorrelation characteristics of super gaussian optical pulse with gaussian optical pulse.
when the signals are symmetric then this signals are gaussian In statistics, the Gaussian curve, also known as the Normal curve, is symmetrical.
copula
A Gaussian noise is a type of statistical noise in which the amplitude of the noise follows that of a Gaussian distribustion whereas additive white Gaussian noise is a linear combination of a Gaussian noise and a white noise (white noise has a flat or constant power spectral density).
Jon Gregory of BNP Paribas and Jean-Paul Laurent of Univeristy of Lyon and BNP Paribas in their paper, "In the Core of Correlation," indicate the appeal of the Guassian single-factor Copula Model relates to: (i) its ease of implementing via Monte Carlo simulation, (ii) the speed with which prices and deltas can be determined, and (iii) its underlying dependence structure has been linked to equity returns correlation.