[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 Center/Unit :
Systèmes et modélisation
Disciplines :
Computer science
Author, co-author :
Schnitzler, François ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Wehenkel, Louis ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Constraint Based Learning of Mixtures of Trees
Publication date :
2009
Number of pages :
3
Event name :
Probabilistic graphical models for integration of complex data and discovery of causal models in biology
Event place :
Nantes, France
Audience :
International
Funders :
FRIA - Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture
