On the determination of probability density functions by using Neural Networks

Lluís Garrido, Aurelio Juste

Research output: Contribution to journalArticleResearchpeer-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 languageEnglish
Pages (from-to)25-31
JournalComputer Physics Communications
Volume115
Issue number1
DOIs
Publication statusPublished - 1 Dec 1998

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