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.

对高维多元分布建模是一项具有计算挑战性的任务。在离散情况下，贝叶斯网络已成功用于降低复杂性并简化问题。然而，他们缺乏连续变量的通用模型。为了克服这个问题，Elidan (2010) 提出了使用 copula 函数参数化贝叶斯网络的 copula 贝叶斯网络模型。我们基于 PC 算法和 Bouezmarni 等人提出的条件独立性测试为该模型提出了一种新的学习算法。(2009)。该测试是非参数的，没有做出模型假设，使其尽可能通用。该算法在生成数据上与 Elidan (2010) 提出的参数化方法进行了比较，并证明具有更好的结果。