Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual demgmm5 Daniel@0: Daniel@0: Daniel@0: Daniel@0:

demgmm5 Daniel@0:

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Daniel@0: Purpose Daniel@0:

Daniel@0: Demonstrate density modelling with a PPCA mixture model. Daniel@0: Daniel@0:

Daniel@0: Synopsis Daniel@0:

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Daniel@0: demgmm5

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Daniel@0: Description Daniel@0:

Daniel@0: Daniel@0: The problem consists of modelling data generated Daniel@0: by a mixture of three Gaussians in 2 dimensions with a mixture model Daniel@0: using full covariance matrices. The priors are 0.3, 0.5 and 0.2; the Daniel@0: centres are (2, 3.5), (0, 0) and (0,2); the variances are (0.16, 0.64) Daniel@0: axis aligned, (0.25, 1) rotated by 30 degrees and the identity Daniel@0: matrix. The first figure contains a scatter plot of the data. Daniel@0: Daniel@0:

A mixture model with three one-dimensional PPCA components is trained Daniel@0: using EM. The parameter vector is printed before training and after Daniel@0: training. The parameter vector consists of priors (the column), and Daniel@0: centres (given as (x, y) pairs as the next two columns). Daniel@0: Daniel@0:

The second figure is a 3 dimensional view of the density function, Daniel@0: while the third shows the axes of the 1-standard deviation ellipses Daniel@0: for the three components of the mixture model together with the one Daniel@0: standard deviation along the principal component of each mixture Daniel@0: model component. Daniel@0: Daniel@0:

Daniel@0: See Also Daniel@0:

Daniel@0: gmm, gmminit, gmmem, gmmprob, ppca

Daniel@0: Pages: Daniel@0: Index Daniel@0:

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