• Corpus ID: 239050060

Joint Gaussian Graphical Model Estimation: A Survey

  title={Joint Gaussian Graphical Model Estimation: A Survey},
  author={Katherine Tsai and Oluwasanmi Koyejo and Mladen Kolar},
Graphs from complex systems often share a partial underlying structure across domains while retaining individual features. Thus, identifying common structures can shed light on the underlying signal, for instance, when applied to scientific discoveries or clinical diagnoses. Furthermore, growing evidence shows that the shared structure across domains boosts the estimation power of graphs, particularly for high-dimensional data. However, building a joint estimator to extract the common structure… 

Figures and Tables from this paper


Joint estimation of multiple graphical models.
An estimator forGaussian graphical models appropriate for data from several graphical models that share the same variables and some of the dependence structure is developed, aiming to preserve the common structure, while allowing for differences between the categories.
Bayesian Inference of Multiple Gaussian Graphical Models
This article addresses the problem of inferring multiple undirected networks in situations where some of the networks may be unrelated, while others share common features, and proposes a Bayesian approach to inference on multiple Gaussian graphical models.
Joint Estimation of Multiple Graphical Models from High Dimensional Time Series.
A kernel based method is proposed for jointly estimating all graphical models in high dimensions that characterizes the strength one can borrow across different individuals and the impact of data dependence on parameter estimation.
Structure Learning in Graphical Modeling
This work discusses methods such as the graphical lasso and neighborhood selection for undirected graphical models, and the PC algorithm and score-based search methods for directed graphical models (or Bayesian networks), and reviews extensions that account for effects of latent variables and heterogeneous data sources.
Regularized Estimation of Piecewise Constant Gaussian Graphical Models: The Group-Fused Graphical Lasso
This work introduces sparsity and sparse-difference inducing priors and proposes a novel regularized M-estimator to jointly estimate both the graph and changepoint structure of a piecewise-constant Gaussian graphical model.
Simultaneous Clustering and Estimation of Heterogeneous Graphical Models
A non-asymptotic error bound is established for the output directly from the high dimensional ECM algorithm, and it consists of two quantities: statistical error (statistical accuracy) and optimization error (computational complexity).
Joint Structural Estimation of Multiple Graphical Models
This work develops methodology that jointly estimates multiple Gaussian graphical models, assuming that there exists prior information on how they are structurally related, and establishes consistency of the proposed method for sparse high-dimensional Gaussia graphical models.
Structured Learning of Gaussian Graphical Models
This work proposes to solve the perturbed-node joint graphical lasso, a convex optimization problem that is based upon the use of a row-column overlap norm penalty, and solves the convex problem using an alternating directions method of multipliers algorithm.
High-dimensional joint estimation of multiple directed Gaussian graphical models
We consider the problem of jointly estimating multiple related directed acyclic graph (DAG) models based on high-dimensional data from each graph. This problem is motivated by the task of learning
Direct Estimation of Differential Functional Graphical Models
A method is developed that directly estimates the difference of graphs, avoiding separate estimation of each graph, and is consistent in certain high-dimensional settings and applies to EEG data to uncover differences in functional brain connectivity between alcoholics and control subjects.