论文标题
贝叶斯网络中节点的高斯混合物建模，根据最大父母集团
Gaussian mixture modeling of nodes in Bayesian network according to maximal parental cliques
论文作者
论文摘要
本文使用高斯混合模型代替线性高斯模型来适合贝叶斯网络中每个节点的分布。我们将解释为什么以及如何在贝叶斯网络中使用高斯混合模型。同时，我们提出了一种称为“双重迭代算法”的新方法，以优化混合模型，双重迭代算法结合了期望最大化算法和梯度下降算法，并且在贝叶斯网络上与混合模型在贝叶斯网络上的性能完美。在实验中，我们测试了高斯混合模型和在不同图表上的优化算法，这些算法是由不同结构学习算法在实际数据集中生成的，并提供了每个实验的详细信息。
This paper uses Gaussian mixture model instead of linear Gaussian model to fit the distribution of every node in Bayesian network. We will explain why and how we use Gaussian mixture models in Bayesian network. Meanwhile we propose a new method, called double iteration algorithm, to optimize the mixture model, the double iteration algorithm combines the expectation maximization algorithm and gradient descent algorithm, and it performs perfectly on the Bayesian network with mixture models. In experiments we test the Gaussian mixture model and the optimization algorithm on different graphs which is generated by different structure learning algorithm on real data sets, and give the details of every experiment.
