Gaussian graphical models depict the conditional dependencies between variables within a multivariate normal distribution in a graphical format. The identification of these graph structures is an area known as structure learning. However, when utilizing Bayesian methodologies in structure learning, computational complexities can arise, especially with high-dimensional graphs surpassing 250 nodes. This paper introduces two innovative search algorithms that employ marginal pseudo-likelihood to address this computational challenge. These methods can swiftly generate reliable estimations for problems encompassing 1000 variables in just a few minutes on standard computers. For those interested in practical applications, the code supporting this new approach is made available through the R package BDgraph.

翻译：高斯图模型以图形形式展示多元正态分布中变量间的条件依赖关系。识别这些图结构的研究领域被称为结构学习。然而，在结构学习中采用贝叶斯方法时，特别是在处理超过250个节点的高维图时，会面临计算复杂性问题。本文提出了两种创新搜索算法，通过利用边缘伪似然来解决这一计算挑战。这些方法能够在标准计算机上仅需几分钟便快速生成包含1000个变量问题的可靠估计。对于有实际应用需求的研究人员，本新方法支持的代码已通过R包BDgraph公开提供。