Constraint-based learning for non-parametric continuous bayesian networks View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-06-26

AUTHORS

Marvin Lasserre, Régis Lebrun, Pierre-Henri Wuillemin

ABSTRACT

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, Elidan (2010) 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 Bouezmarni et al. (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 parametric method proposed by Elidan (2010) and proves to have better results. More... »

PAGES

1035-1052

References to SciGraph publications

  • 2002. Random Generation of Bayesian Networks in ADVANCES IN ARTIFICIAL INTELLIGENCE
  • 1998. Tabu Search in HANDBOOK OF COMBINATORIAL OPTIMIZATION
  • 2010-05-25. Pair-Copula Constructions of Multivariate Copulas in COPULA THEORY AND ITS APPLICATIONS
  • 2015. OpenTURNS: An Industrial Software for Uncertainty Quantification in Simulation in HANDBOOK OF UNCERTAINTY QUANTIFICATION
  • 2003. Kendall’s Tau for Elliptical Distributions in CREDIT RISK
  • 1989. The ALARM Monitoring System: A Case Study with two Probabilistic Inference Techniques for Belief Networks in AIME 89
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    URI

    http://scigraph.springernature.com/pub.10.1007/s10472-021-09754-2

    DOI

    http://dx.doi.org/10.1007/s10472-021-09754-2

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