Conditional density approximations with mixtures of polynomials

Varando, Gherardo and López Cruz, Pedro Luis and Nielsen, Thomas D. and Larrañaga Múgica, Pedro and 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.

Description

Title: Conditional density approximations with mixtures of polynomials
Author/s:
  • Varando, Gherardo
  • López Cruz, Pedro Luis
  • Nielsen, Thomas D.
  • Larrañaga Múgica, Pedro
  • Bielza Lozoya, Maria Concepcion
Item Type: Article
Título de Revista/Publicación: International Journal of Intelligent Systems
Date: 2014
ISSN: 0884-8173
Volume: 30
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainC080020-09UnspecifiedUnspecifiedUnspecified
Government of SpainTIN2013-41592-PUnspecifiedUnspecifiedUnspecified
Madrid Regional GovernmentS2013/ICE-2845-CASI-CAM-CMUnspecifiedUnspecifiedUnspecified

More information

Item ID: 35617
DC Identifier: http://oa.upm.es/35617/
OAI Identifier: oai:oa.upm.es:35617
DOI: 10.1002/int.21699
Official URL: http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291098-111X
Deposited by: Memoria Investigacion
Deposited on: 01 Feb 2016 09:35
Last Modified: 21 May 2019 11:10
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