wolffd@0: wolffd@0: wolffd@0: wolffd@0: Netlab Reference Manual demgmm3 wolffd@0: wolffd@0: wolffd@0: wolffd@0:

demgmm3 wolffd@0:

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wolffd@0: Purpose wolffd@0:

wolffd@0: Demonstrate density modelling with a Gaussian mixture model. wolffd@0: wolffd@0:

wolffd@0: Synopsis wolffd@0:

wolffd@0:

wolffd@0: demgmm3

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wolffd@0: Description wolffd@0:

wolffd@0: wolffd@0: The problem consists of modelling data generated wolffd@0: by a mixture of three Gaussians in 2 dimensions with a mixture model wolffd@0: using diagonal covariance matrices. The priors are 0.3, 0.5 and 0.2; the wolffd@0: centres are (2, 3.5), (0, 0) and (0,2); the covariances are all axis aligned wolffd@0: (0.16, 0.64), (0.25, 1) and the identity wolffd@0: matrix. The first figure contains a scatter plot of the data. wolffd@0: wolffd@0:

A Gaussian mixture model with three components is trained using EM. The wolffd@0: parameter vector is printed before training and after training. The user wolffd@0: should press any key to continue at these points. The parameter vector wolffd@0: consists of priors (the column), and centres (given as (x, y) pairs as wolffd@0: the next two columns). The diagonal entries of the wolffd@0: covariance matrices are printed separately. wolffd@0: wolffd@0:

The second figure is a 3 dimensional view of the density function, wolffd@0: while the third shows the axes of the 1-standard deviation circles wolffd@0: for the three components of the mixture model. wolffd@0: wolffd@0:

wolffd@0: See Also wolffd@0:

wolffd@0: gmm, gmminit, gmmem, gmmprob, gmmunpak

wolffd@0: Pages: wolffd@0: Index wolffd@0:

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