On the determination of probability density functions by using Neural Networks

Abstract

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.

13 pages including 3 eps figures. Submitted to Comput. Phys. Commun

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