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IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 3, MAY 2002 497 Density Estimation and Random Variate Generation
 

Summary: IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 3, MAY 2002 497
Density Estimation and Random Variate Generation
Using Multilayer Networks
Malik Magdon-Ismail, Member, IEEE, and Amir Atiya, Senior Member, IEEE
Abstract--In this paper we consider two important topics: den-
sity estimation and random variate generation. We will present
a framework that is easily implemented using the familiar multi-
layer neural network. First, we develop two new methods for den-
sity estimation, a stochastic method and a related deterministic
method. Both methods are based on approximating the distribu-
tion function, the density being obtained by differentiation. In the
second part of the paper, we develop new random number genera-
tion methods. Our methods do not suffer from some of the restric-
tions of existing methods in that they can be used to generate num-
bers from any density provided that certain smoothness conditions
are satisfied. One of our methods is based on an observed inverse
relationship between the density estimation process and random
number generation. We present two variants of this method, a sto-
chastic, and a deterministic version. We propose a second method
that is based on a novel control formulation of the problem, where

  

Source: Atiya, Amir - Computer Engineering Department, Cairo University

 

Collections: Computer Technologies and Information Sciences