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Article Dans Une Revue Annals of Mathematics and Artificial Intelligence Année : 2021

Constraint-based learning for non-parametric continuous bayesian networks

Marvin Lasserre
  • Fonction : Auteur
Pierre-Henri Wuillemin

Résumé

Modeling high-dimensional multivariate distributions is a computationally challenging task. In the discrete case, Bayesian networks have been successfully used to reduce the complexity and to simplify the problem. However, they lack of a general model for continuous variables. In order to overcome this problem, [9] proposed the model of copula Bayesian networks that parametrizes Bayesian networks using copula functions. We propose a new learning algorithm for this model based on a PC algorithm and a conditional independence test proposed by [4]. This test being non-parametric, no model assumptions are made allowing it to be as general as possible. This algorithm is compared on generated data with the parametric method proposed by [9] and proves to have better results.
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Dates et versions

hal-03272627 , version 1 (28-06-2021)

Identifiants

Citer

Marvin Lasserre, Régis Lebrun, Pierre-Henri Wuillemin. Constraint-based learning for non-parametric continuous bayesian networks. Annals of Mathematics and Artificial Intelligence, 2021, 89, pp.1035-1052. ⟨10.1007/s10472-021-09754-2⟩. ⟨hal-03272627⟩
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