Several different structures for the model were tested. The number of source signals and the number of Gaussians in the mixtures was varied. The number of Gaussians in the mixtures was same for all sources in each network. This is not to say that it would not be perfectly simple to optimise the number of Gaussians for each source separately.

It turned out that the Kullback-Leibler information was minimised by a
network with 23 dimensional source signals whose distributions were
mixtures of three Gaussians. There were 87 292 unknown variables in
the model: 86 227 in *s*(*t*); 782 in *A*; 68 in *b* and ; 207
in *c*, *S* and ; and 8 in hyperparameters.

Figures 1-3 show the basis vectors, histograms and reconstructed distributions of the 23 source signals of the best network. The ordering in all three figures is the same. The basis vectors in figure 1 probably do not seem very interesting for someone who is not working with speech recognition. The basis is fairly close to cosine transformation which is widely used for processing the spectra in speech recognition.

The histograms in figure 2 and the corresponding distributions in figure 3 show that the algorithm works. The model has captures the salient features of the source distributions, some of which are multimodal, skewed or kurtotic.

The second best fit was obtained by a network with two Gaussian in the
mixtures, but the probability mass it captured^{} was over 10^{76} times smaller! It is
therefore reasonable to approximate the whole posterior pdf of all
model structures and parameters by an ensemble with a peak in only one
model structure. It is also evident that in this case, any prior
information about the model structure has no significance.

For comparison, also models with only one Gaussian in the mixtures
were tested. In this case the logarithmic mixture coefficients
*c*_{ij} can be dropped out from the model. The best model with only
one Gaussian was found to have over 10^{1238} times less probability
mass. This shows that the algorithm agrees with human eye: it is
clear that the distributions of at least some of the source signals
are far from Gaussian.