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Constraint-Based Learning for Non-Parametric Continuous Bayesian Networks

Abstract : Modeling high-dimensional multivariate distributions is a computationally challenging task. Bayesian networks have been successfully used to reduce the complexity and simplify the problem with discrete variables. However, it lacks of a general model for continuous variables. In order to overcome this problem, (Elidan 2010) proposed the model of cop-ula bayesian networks that reparametrizes bayesian networks with conditional copula functions. We propose a new learning algorithm for copula bayesian networks based on a PC algorithm and a conditional independence test proposed by (Bouezmarni, Rombouts, and Taamouti 2009). 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 score based method proposed by (El-idan 2010). Not only it proves to be faster, but also it generalizes well on data generated from distributions far from the gaussian model.
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Submitted on : Friday, May 22, 2020 - 4:18:17 PM
Last modification on : Sunday, June 26, 2022 - 2:49:53 AM

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  • HAL Id : hal-02615379, version 1

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Marvin Lasserre, Régis Lebrun, Pierre-Henri Wuillemin. Constraint-Based Learning for Non-Parametric Continuous Bayesian Networks. FLAIRS 33 - 33rd Florida Artificial Intelligence Research Society Conference, May 2020, Miami, United States. pp.581-586. ⟨hal-02615379⟩

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