On the determination of probability density functions by using Neural Networks

Details On the determination of probability density functions by using Neural Networks On the determination of probability density functions by using Neural Networks

1998-07-14

arxiv.org/abs/physics/9807018v1

Physics

Data Analysis, Statistics and Probability

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

Scientific paper

10.1016/S0010-4655(98)00107-6

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.

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