Gaussian graphical models (GGMs) are a popular form of network model in which nodes represent features in multivariate normal data and edges reflect conditional dependencies between these features. GGM estimation is an active area of research. Currently available tools for GGM estimation require ...
In this article, we develop a penalized fusion approach for heterogeneity analysis based on the Gaussian Graphical Model (GGM). It applies penalization to the mean and precision matrix parameters to generate regularized and interpretable estimates. More importantly, a fusion penalty is imposed to "...
graphical Gaussian models 高斯图模型 高斯图模型(GGM),是研究基因关联网络的流行工具, 了解GGMs的最佳起点是20世纪70年代早期引入这一概念的经典论文。(AP Dempster.1972。协方差选择。生物统计学28:157-175)。进一步的细节可以在J.Whittaker(1990)和D.Edwards(1995)的GGM书中找到。 高斯图模型是概率图模型的一种...
Network properties of the correlation network (CN) and Gaussian graphical model (GGM) inferred from a targeted metabolomics population data set (1020 participants, 151 quantified metabolites). A+B: Graphical depiction of significantly positive edges in both networks, emphasizing local clustering structures...
内容提示: Gaussian Graphical Model Estimation with FalseDiscovery Rate Control ∗Weidong LiuAbstractThis paper studies the estimation of high dimensional Gaussian graphical model(GGM). Typically, the existing methods depend on regularization techniques. Asa result, it is necessary to choose the ...
convert it to a GGM so we have a proper joint density model • We use the inferred clusterings argmax k p(z d =k) to define the groups, and use group L1 penalized MAP estimation to estimate Ω. • We estimate λ by CV. Results on mocap data • D=60 (20 markers in...
Specifically, we impose a bipartite structure on the GGM and govern the hidden variables by truncated normal distributions. The nonlinearity of the model is revealed by its connection to rectified linear unit (ReLU) neural networks. Meanwhile, thanks to the bipartite structure and appealing properties...
Gaussian graphical model (GGM) is a relatively more realistic way to present complex network because of its interpretation with conditional dependence between two variables after removing the effects of all other variables5. GGM can filter out all high correlations which are attributed to other genes...
This paper studies the estimation of high dimensional Gaussian graphical model (GGM). Typically, the existing methods depend on regularization techniques. As a result, it is necessary to choose the regularized parameter. However, the precise relationship between the regularized parameter and the number...
Starch serves as a temporal storage of carbohydrates in plant leaves during day/night cycles. To study transcriptional regulatory modules of this dynamic metabolic process, we conducted gene regulation network analysis based on small-sample inference of graphical Gaussian model (GGM). ...