高斯图模型与 graphical lasso 的最大似然阈值
Maximum likelihood thresholds of Gaussian graphical models and graphical lasso
AI总结：
本研究针对高维高斯图模型，考察用 graphical lasso 在 n 个样本上选择并拟合模型时，n 达到对应图最大似然阈值的概率，并通过计算实验给出结果。
摘要：
每个图 G 都关联着一个高斯图模型。这类模型常用于高维场景，即与变量数量相比数据点相对较少的情形。图的最大似然阈值是指使用最大似然估计来拟合相应图模型所需的最少数据点数量。Graphical lasso 是一种用于选择并拟合图模型的方法。在本项目中，我们提出如下问题：当使用 graphical lasso 在 n 个数据点上选择并拟合一个图模型时，n 大于或等于相应图的最大似然阈值的可能性有多大？我们的结果是一系列计算实验。
英文摘要：
Associated to each graph G is a Gaussian graphical model. Such models are often used in high-dimensional settings, i.e. where there are relatively few data points compared to the number of variables. The maximum likelihood threshold of a graph is the minimum number of data points required to fit the corresponding graphical model using maximum likelihood estimation. Graphical lasso is a method for selecting and fitting a graphical model. In this project, we ask: when graphical lasso is used to select and fit a graphical model on n data points, how likely is it that n is greater than or equal to the maximum likelihood threshold of the corresponding graph? Our results are a series of computational experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
