The most famous statistical mixture consists of the family of Gaussian mixtures.
In the literature, they bear the names of
The applet below generates random GMMs
(k components in d dimension with separability factor epsilon)
according to the Wishart distribution as follows:
- GMMs: Gaussian mixture models, or
- MoGs: Mixtures of Gaussians.
- The weights w[i] are drawn uniformly from [0,1] (and renormalized so that they sum up to 1),
- The means mu[i] are also sampled from a uniform distribution in [0,1],
- The variance-covariance matrix Sigma[i] (positive definite matrix) is computed as A A^T where the elements are drawn
from a normal distribution N(0,1) of unit variance centered at zero.
The matrix elements are scaled by a factor epsilon.
A smaller value of epsilon separates more the components of the Gaussians.
Frank Nielsen, September 2008.