Reference : Constraint Based Learning of Mixtures of Trees

	Document type :	Scientific congresses and symposiums : Unpublished conference
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/2268/36567

Title :	Constraint Based Learning of Mixtures of Trees
Language :	English
Author, co-author :	Schnitzler, François [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
	Wehenkel, Louis [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Publication date :	2009
Page number :	3
Audience :	International
Event name :	Probabilistic graphical models for integration of complex data and discovery of causal models in biology
Event place (city) :	Nantes
Event country :	France
Keywords :	[en] mixture ; trees ; causal learning
Abstract :	[en] Mixtures of trees can be used to model any multivariate distributions. In this work the possibility to learn these models from data by causal learning is explored. The algorithm developed aims at approximating all first order relationships between pairs of variables by a mixture of a given size. This approach is evaluated based on synthetic data, and seems promising.
Research units :	Systèmes et modélisation
Funders :	Fonds pour la formation à la Recherche dans l'Industrie et dans l'Agriculture (Communauté française de Belgique) - FRIA
Target :	Researchers
Permalink :	http://hdl.handle.net/2268/36567

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	Fulltext file(s): File Commentary Version Size Access Open access soumission_Modgraph.pdf Author preprint 83.24 kB View/Open Additional material(s): File Commentary Version Size Access Open access presentation1.pdf presentation - 740.72 kB View/Open

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