Fusión de redes Bayesianas Gaussianas

Córdoba Sánchez, Irene (2015). Fusión de redes Bayesianas Gaussianas. Thesis (Master thesis), E.T.S. de Ingenieros Informáticos (UPM).

Description

Title: Fusión de redes Bayesianas Gaussianas
Author/s:
  • Córdoba Sánchez, Irene
Contributor/s:
  • Bielza Lozoya, Concepción
  • Larrañaga Múgica, Pedro
Item Type: Thesis (Master thesis)
Masters title: Inteligencia Artificial
Date: December 2015
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

Las redes Bayesianas constituyen un modelo ampliamente utilizado para la representación de relaciones de dependencia condicional en datos multivariantes. Su aprendizaje a partir de un conjunto de datos o expertos ha sido estudiado profundamente desde su concepción. Sin embargo, en determinados escenarios se demanda la obtención de un modelo común asociado a particiones de datos o conjuntos de expertos. En este caso, se trata el problema de fusión o agregación de modelos. Los trabajos y resultados en agregación de redes Bayesianas son de naturaleza variada, aunque escasos en comparación con aquellos de aprendizaje. En este documento, se proponen dos métodos para la agregación de redes Gaussianas, definidas como aquellas redes Bayesianas que modelan una distribución Gaussiana multivariante. Los métodos presentados son efectivos, precisos y producen redes con menor cantidad de parámetros en comparación con los modelos obtenidos individualmente. Además, constituyen un enfoque novedoso al incorporar nociones exploradas tradicionalmente por separado en el estado del arte. Futuras aplicaciones en entornos escalables hacen dichos métodos especialmente atractivos, dada su simplicidad y la ganancia en compacidad de la representación obtenida.---ABSTRACT---Bayesian networks are a widely used model for the representation of conditional dependence relationships among variables in multivariate data. The task of learning them from a data set or experts has been deeply studied since their conception. However, situations emerge where there is a need of obtaining a consensuated model from several data partitions or a set of experts. This situation is referred to as model fusion or aggregation. Results about Bayesian network aggregation, although rich in variety, have been scarce when compared to the learning task. In this context, two methods are proposed for the aggregation of Gaussian Bayesian networks, that is, Bayesian networks whose underlying modelled distribution is a multivariate Gaussian. Both methods are effective, precise and produce networks with fewer parameters in comparison with the models obtained by individual learning. They constitute a novel approach given that they incorporate notions traditionally explored separately in the state of the art. Future applications in scalable computer environments make such models specially attractive, given their simplicity and the gaining in sparsity of the produced model.

Funding Projects

TypeCodeAcronymLeaderTitle
Madrid Regional GovernmentS2013/ICE- 2845-CASI-CAM-CMUnspecifiedUnspecifiedUnspecified

More information

Item ID: 39091
DC Identifier: http://oa.upm.es/39091/
OAI Identifier: oai:oa.upm.es:39091
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 21 Jan 2016 11:18
Last Modified: 21 Jan 2016 11:18
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