Memorias de investigación
Research Publications in journals:
Conditional density approximations with mixtures of polynomials
Year:2014

Research Areas
  • Artificial intelligence

Information
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.
International
Si
JCR
Si
Title
International Journal of Intelligent Systems
ISBN
0884-8173
Impact factor JCR
1,416
Impact info
Datos JCR del año 2012
Volume
30
Journal number
3
From page
236
To page
264
Month
SIN MES
Ranking
Participants

Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Computational Intelligence Group
  • Departamento: Inteligencia Artificial