TY - JOUR

T1 - On the determination of probability density functions by using Neural Networks

AU - Garrido, Lluís

AU - Juste, Aurelio

PY - 1998/12/1

Y1 - 1998/12/1

N2 - 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.

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.

U2 - 10.1016/S0010-4655(98)00107-6

DO - 10.1016/S0010-4655(98)00107-6

M3 - Article

VL - 115

SP - 25

EP - 31

JO - Computer Physics Communications

JF - Computer Physics Communications

SN - 0010-4655

IS - 1

ER -