On the determination of probability density functions by using Neural Networks Original Research Article

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 performance than “standard binned procedures”. In addition, the mapped p.d.f. has an analytical expression.