The most famous statistical mixture consists of the family of Gaussian mixtures.
In the literature, they bear the names of

• GMMs: Gaussian mixture models, or
• MoGs: Mixtures of Gaussians.

The applet below generates random GMMs (k components in d dimension with separability factor epsilon) according to the Wishart distribution as follows:

• 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.

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Frank Nielsen, September 2008.