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.

翻译：本文使用高斯混合模型, 而不是线性高斯模型, 以适应巴伊西亚网络中每个节点的分布。 我们将解释为什么和如何在巴伊西亚网络中使用高斯混合模型。 与此同时, 我们提出一种新的方法, 称为双迭代算法, 优化混合模型, 双迭代算法将预期最大化算法和梯度下行算法结合起来, 并在巴伊西亚网络上与混合模型完美地运行。 在实验中, 我们测试高斯混合物模型和不同结构对真实数据集的学习算法所产生的不同图表的优化算法, 并给出每次实验的细节 。