Conditional density approximations with mixtures of polynomials

Varando, Gherardo; López Cruz, Pedro Luis; Nielsen, Thomas D.; Larrañaga Múgica, Pedro y Bielza Lozoya, Maria Concepcion (2014). Conditional density approximations with mixtures of polynomials. "International Journal of Intelligent Systems", v. 30 (n. 3); pp. 236-264. ISSN 0884-8173. https://doi.org/10.1002/int.21699.

Descripción

Título: Conditional density approximations with mixtures of polynomials
Autor/es:
  • Varando, Gherardo
  • López Cruz, Pedro Luis
  • Nielsen, Thomas D.
  • Larrañaga Múgica, Pedro
  • Bielza Lozoya, Maria Concepcion
Tipo de Documento: Artículo
Título de Revista/Publicación: International Journal of Intelligent Systems
Fecha: 2014
Volumen: 30
Materias:
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 35617
Identificador DC: http://oa.upm.es/35617/
Identificador OAI: oai:oa.upm.es:35617
Identificador DOI: 10.1002/int.21699
URL Oficial: http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291098-111X
Depositado por: Memoria Investigacion
Depositado el: 01 Feb 2016 09:35
Ultima Modificación: 17 Nov 2017 08:57
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