We derive Laplace-approximated maximum likelihood estimators (GLAMLEs) of parameters in our Graph Generalized Linear Latent Variable Models. Then, we study the statistical properties of GLAMLEs when the number of nodes $n_V$ and the observed times of a graph denoted by $K$ diverge to infinity. Finally, we display the estimation results in a Monte Carlo simulation considering different numbers of latent variables. Besides, we make a comparison between Laplace and variational approximations for inference of our model.

翻译：我们从“总线性边端变量模型”中得出参数的拉皮尔-近似最大概率估计器(GLAMLES)。 然后,当节点数为$_V$和用K$表示的图表的观察时间与无限值不同时,我们研究GLAMLE的统计属性。 最后,我们用蒙特卡洛模拟模型来显示估计结果,其中考虑到不同数量的潜伏变量。此外,我们比较拉皮尔和变差近似值以推断模型。