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Communication Dans Un Congrès Année : 2007

On Directed and Undirected Propagation Algorithms for Bayesian Networks

Christophe Gonzales
Khaled Mellouli
  • Fonction : Auteur
Olfa Mourali
  • Fonction : Auteur

Résumé

Message-passing inference algorithms for Bayes nets can be broadly divided into two classes: i) clustering algorithms, like Lazy Propagation, Jensen’s or Shafer-Shenoy’s schemes, that work on secondary undirected trees; and ii) conditioning methods, like Pearl’s, that use directly Bayes nets. It is commonly thought that algorithms of the former class always outperform those of the latter because Pearl’s-like methods act as particular cases of clustering algorithms. In this paper, a new variant of Pearl’s method based on a secondary directed graph is introduced, and it is shown that the computations performed by Shafer-Shenoy or Lazy propagation can be precisely reproduced by this new variant, thus proving that directed algorithms can be as efficient as undirected ones.

Dates et versions

hal-01311629 , version 1 (04-05-2016)

Identifiants

Citer

Christophe Gonzales, Khaled Mellouli, Olfa Mourali. On Directed and Undirected Propagation Algorithms for Bayesian Networks. European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Oct 2007, Hammamet, Tunisia. pp.598-610, ⟨10.1007/978-3-540-75256-1_53⟩. ⟨hal-01311629⟩
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