On the determination of probability density functions by using Neural Networks
Data Analysis, Statistics and Probability
2009-10-31 v1
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.
Cite
@article{arxiv.physics/9807018,
title = {On the determination of probability density functions by using Neural Networks},
author = {Lluis Garrido and Aurelio Juste},
journal= {arXiv preprint arXiv:physics/9807018},
year = {2009}
}
Comments
13 pages including 3 eps figures. Submitted to Comput. Phys. Commun