  It is well known that the output of a Neural Network trained to disentangle
between two classes has a probabilistic interpretation in terms of the
a-posteriori Bayesian probability, provided that a unary representation is
taken for the output patterns. This fact is used to make Neural Networks
approximate probability density functions from examples in an unbinned way,
giving a better performace than ``standard binned procedures''. In addition,
the mapped p.d.f. has an analytical expression.
