We propose a graphical structure for structural equation models that is stable under marginalization under linearity and Gaussianity assumptions. We show that computing the maximum likelihood estimation of this model is equivalent to training a neural network. We implement a GPU-based algorithm that computes the maximum likelihood estimation of these models.

翻译：我们提出了一种在线性和高斯性假设下边缘化时保持稳定的结构方程模型图结构。研究表明，该模型的最大似然估计计算等价于训练神经网络。我们实现了一种基于GPU的算法，用于计算此类模型的最大似然估计。