Learning Bayesian networks with low inference complexity

Benjumeda Barquita, Marco Alberto and Larrañaga Múgica, Pedro María and Bielza Lozoya, María Concepción (2016). Learning Bayesian networks with low inference complexity. "Progress in Artificial Intelligence", v. 5 (n. 1); pp. 15-26. ISSN 2192-6360. https://doi.org/10.1007/s13748-015-0070-0.

Description

Title: Learning Bayesian networks with low inference complexity
Author/s:
  • Benjumeda Barquita, Marco Alberto
  • Larrañaga Múgica, Pedro María
  • Bielza Lozoya, María Concepción
Item Type: Article
Título de Revista/Publicación: Progress in Artificial Intelligence
Date: 2016
ISSN: 2192-6360
Volume: 5
Subjects:
Freetext Keywords: Probabilistic graphical models; Bayesian networks; Arithmetic circuits; Network polynomials; Structure learning; Thin models
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

One of the main research topics in machine learning nowadays is the improvement of the inference and learning processes in probabilistic graphical models. Traditionally, inference and learning have been treated separately, but given that the structure of the model conditions the inference complexity, most learning methods will sometimes produce inefficient inference models. In this paper we propose a framework for learning low inference complexity Bayesian networks. For that, we use a representation of the network factorization that allows efficiently evaluating an upper bound in the inference complexity of each model during the learning process. Experimental results show that the proposed methods obtain tractable models that improve the accuracy of the predictions provided by approximate inference in models obtained with a well-known Bayesian network learner.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTIN2013-41592-PUnspecifiedUniversidad Politécnica de MadridAprendizaje de redes bayesianas con variables sin y con direccionalidad para descubrimiento de asociaciones, predicción multirespuesta y clustering
Madrid Regional GovernmentS2013/ICE-2845CASI - CAMAníbal Ramón Figueiras VidalConceptos y Aplicaciones de los Sistemas Inteligentes
FP7604102HBPECOLE POLYTECHNIQUE FEDERALE DE LAUSANNEThe Human Brain Project
Government of SpainC080020-09UnspecifiedUnspecifiedCajal Blue Brain

More information

Item ID: 41178
DC Identifier: http://oa.upm.es/41178/
OAI Identifier: oai:oa.upm.es:41178
DOI: 10.1007/s13748-015-0070-0
Official URL: https://doi.org/10.1007/s13748-015-0070-0
Deposited by: Memoria Investigacion
Deposited on: 20 Oct 2016 10:10
Last Modified: 14 Mar 2019 16:23
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