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Generating Random Variates via Kernel Density Estimation and Radial Basis Function Based Neural Networks

When modeling phenomena that cannot be studied by deterministic analytical approaches, one of the main tasks is to generate random variates. The widely-used techniques, such as the inverse transformation, convolution, and rejection-acceptance methods, involve a significant amount of statistical work...

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Bibliographic Details
Published in:Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Institution:Escuela Colombiana de Ingeniería
Main Authors: Candia García, Cristian, Forero, Manuel G., Herrera Rivera, Sergio
Format: Capítulo - Parte de Libro
Language:English
Published: Springer Nature 2019
Subjects:
Variable aleatoria
Distribución de probabilidad
Teoría de las distribuciones (Análisis funcional)
Funciones Kernel
Theory of distributions (Functional analysis)
Kernel functions
General regression neural network
Probabilistic neural network
Kernel density estimation
Random variable
Probability distribution
Online Access:https://repositorio.escuelaing.edu.co/handle/001/1605
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