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On the construction of the inclusion boundary neighbourhood for markov equivalence classes of bayesian network structures
Auvray, Vincent; Wehenkel, Louis
2002In Proceedings of Uncertainty in Artificial Intelligence
Peer reviewed
 

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Keywords :
Artificial Intelligence; Graphical models; Machine Learning; Bayesian Networks
Abstract :
[en] The problem of learning Markov equivalence classes of Bayesian network structures may be solved by searching for the maximum of a scoring metric in a space of these classes. This paper deals with the definition and analysis of one such search space. We use a theoretically motivated neighbourhood, the inclusion boundary, and represent equivalence classes by essential graphs. We show that this search space is connected and that the score of the neighbours can be evaluated incrementally. We devise a practical way of building this neighbourhood for an essential graph that is purely graphical and does not explicitely refer to the underlying independences. We find that its size can be intractable, depending on the complexity of the essential graph of the equivalence class. The emphasis is put on the potential use of this space with greedy hillclimbing search.
Disciplines :
Computer science
Author, co-author :
Auvray, Vincent
Wehenkel, Louis  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
On the construction of the inclusion boundary neighbourhood for markov equivalence classes of bayesian network structures
Publication date :
2002
Event name :
Uncertainty in Artificial Intelligence
Event place :
United States
Audience :
International
Main work title :
Proceedings of Uncertainty in Artificial Intelligence
Pages :
10
Peer reviewed :
Peer reviewed
Available on ORBi :
since 17 December 2010

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