The method proposed here has limitations due to the simple structure of the ensemble. The posterior pdf of the unknown variables is often close to Gaussian, but there can be significant correlations. On the other hand, as the algorithm tries to fit the ensemble to the posterior pdf, it tries to find a peak in the posterior which would satisfy the diagonality assumption. In the simulations with only one Gaussian in the mixtures, for instance, the linear mapping found by the algorithm will orthogonal because that makes the sources independent in the posterior pdf.

There are two significant cases where the factorial assumption for the
ensemble is too strong. If the row vectors of the linear mapping *A*
are far from orthogonal, the source signals are correlated in the
posterior pdf. Another case is when the amount of noise in the data
is small and there are not very many data samples. In that case the
components of the linear mapping are correlated with the source
signals in the posterior pdf.

The limitations can be overcome by adding off-diagonal terms to the covariance matrix of the ensemble, but then the formulas for the Kullback-Leibler information become more complicated. Full covariance matrix is, of course, out of the question: the ensemble already had 174 584 parameters and with full covariance matrix the number would have been almost . It is more feasible to try to find model structures which make the off-diagonal terms in the covariance matrix of the unknown variables small.