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Artículos en revistas:
Conditional density approximations with mixtures of polynomials
Año:2014
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
Mixtures of polynomials (MoPs) are a non-parametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn one- and multi-dimensional (marginal) MoPs from data have recently been proposed. In this paper we introduce 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 and study the methods using data sampled from known parametric distributions, and we demonstrate their applicability by learning models based on real neuroscience data. Finally, we compare the performance of the proposed methods with an approach for learning mixtures of truncated basis functions (MoTBFs). The empirical results show that the proposed methods generally yield models that are comparable to or significantly better than those found using the MoTBF-based method.
Internacional
Si
JCR del ISI
Si
Título de la revista
International Journal of Intelligent Systems
ISSN
0884-8173
Factor de impacto JCR
1,416
Información de impacto
Datos JCR del año 2012
Volumen
30
DOI
Número de revista
3
Desde la página
236
Hasta la página
264
Mes
SIN MES
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Gherardo Varando (UPM)
  • Autor: Pedro Luis Lopez Cruz (UPM)
  • Autor: Thomas D. Nielsen (Aalborg University)
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Computational Intelligence Group
  • Departamento: Inteligencia Artificial
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