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We compare two probabilistic approaches to neural networks - the first one based on the mixtures of product  components and the second one using the mixtures of dependence-tree distributions. The product mixture  models can be efficiently estimated from data by means of EM algorithm and have some practically important  properties. However, in some cases the simplicity of product components could appear too restrictive and a  natural idea is to use a more complex mixture of dependence-tree distributions. By considering the concept of  dependence tree we can explicitly describe the statistical relationships between pairs of variables at the level  of individual components and therefore the approximation power of the resulting mixture may essentially  increase. Nonetheless, in application to classification of numerals we have found that both models perform  comparably and the contribution of the dependence-tree structures decreases in the course of EM iterations.  Thus the optimal estimate of the dependence-tree mixture tends to converge to a simple product mixture model.  Regardless of computational aspects, the dependence-tree mixtures could help to clarify the role of dendritic  branching in the highly selective excitability of neurons.

Pattern Recognition by Probabilistic Neural Networks - Mixtures of Product Components versus Mixtures of Dependence Trees