# demgmm5

## Purpose

Demonstrate density modelling with a PPCA mixture model.

## Synopsis

    demgmm5

## Description

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

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

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

## See Also

`gmm`, `gmminit`, `gmmem`, `gmmprob`, `ppca`

Pages: Index

Copyright (c) Ian T Nabney (1996-9)