On the determination of probability density functions by using Neural Networks

Lluís Garrido; Aurelio Juste

doi:10.1016/S0010-4655(98)00107-6

On the determination of probability density functions by using Neural Networks

Lluís Garrido, Aurelio Juste

Department of Physics

Research output: Contribution to journal › Article › Research › peer-review

6 Citations (Scopus)

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 performance than "standard binned procedures". In addition, the mapped p.d.f. has an analytical expression. © 1998 Elsevier Science B.V.

Original language	English
Pages (from-to)	25-31
Journal	Computer Physics Communications
Volume	115
Issue number	1
DOIs	https://doi.org/10.1016/S0010-4655(98)00107-6
Publication status	Published - 1 Dec 1998

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AB - 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. © 1998 Elsevier Science B.V.

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