VIBESVariational Inference for Bayesian Networks 
[Sourceforge project page] John Winn, January 2004 
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4. Extending the Gaussian model to a Gaussian mixture modelOur aim is to create a Gaussian mixture model and so we must extend our simple Gaussian model to be a mixture with K Gaussian components. As there will now be K sets of the latent variables μ and γ, these are placed in a new plate, called K, whose size is set to 20. We modify the conditional distribution for the x node to be a mixture of dimension K, with each component being Gaussian. The display is then as shown below.
The model is currently incomplete as making x a mixture requires
a new discrete Note: If you want to skip constructing this network by hand, it is
in the tutorial file called
