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Memorias de investigación
Capítulo de libro:
Learning mixtures of polynomials of conditional densities from data
Año:2013
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
Mixtures of polynomials (MoPs) are a non-parametric density estimation technique for hybrid Bayesian networks with continuous and discrete variables. We propose two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate the methods using data sampled from a simple Gaussian Bayesian network. We study and compare the performance of these methods with the approach for learning mixtures of truncated basis functions from data.
Internacional
Si
DOI
Edición del Libro
Editorial del Libro
Springer
ISBN
978-3-642-40642-3
Serie
Título del Libro
Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence 8109
Desde página
363
Hasta página
372
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Pedro Luis Lopez Cruz (UPM)
  • Autor: Thomas Nielsen
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Inteligencia Artificial
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