Algorithm 1045: a covariate-dependent approach to Gaussian graphical modeling in R

DOI10.1145/3659206MaRDI QIDQ6604172

Sutanoy Dasgupta, Bani. K. Mallick, Jacob Helwig, Debdeep Pati, Peng Zhao

Publication date: 12 September 2024

Published in: ACM Transactions on Mathematical Software (Search for Journal in Brave)

zbMATH Keywords

pseudo-likelihoodGaussian graphical modelsvariational inferencestructure learningheterogeneous graphs

Mathematics Subject Classification ID

Numerical analysis (65-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Sparse inverse covariance estimation with the graphical lasso
• Estimation of multiple networks in Gaussian mixture models
• Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks
• On bandwidth variation in kernel estimates. A square root law
• An introduction to variational methods for graphical models
• Dynamic and robust Bayesian graphical models
• Quasi-Bayesian estimation of large Gaussian graphical models
• High-dimensional graphs and variable selection with the Lasso
• Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies
• ggplot2
• Estimating Time-Varying Graphical Models
• Bayesian Variable Selection in Linear Regression
• Simultaneous Clustering and Estimation of Heterogeneous Graphical Models
• The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
• Bayesian Graphical Regression
• Bayesian Inference of Multiple Gaussian Graphical Models
• Joint Estimation of Multiple Graphical Models from High Dimensional Time Series
• High-Dimensional Gaussian Graphical Regression Models with Covariates
• Gaussian graphical model‐based heterogeneity analysis via penalized fusion

This page was built for publication: Algorithm 1045: a covariate-dependent approach to Gaussian graphical modeling in R