Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison

Cites Work

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Loglinear model selection and human mobility
Extending the rank likelihood for semiparametric copula estimation
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
Sparse estimation of a covariance matrix
Sparse inverse covariance estimation with the graphical lasso
Bayesian structure learning in sparse Gaussian graphical models
Scaling it up: stochastic search structure learning in graphical models
TIGER: A tuning-insensitive approach for optimally estimating Gaussian graphical models
Learning Gaussian graphical models with fractional marginal pseudo-likelihood
Copula Gaussian graphical models and their application to modeling functional disability data
Bayesian hypothesis testing for Gaussian graphical models: conditional independence and order constraints
The Bayesian covariance lasso
Hyper Markov laws in the statistical analysis of decomposable graphical models
Improving Markov chain Monte Carlo model search for data mining
Bayesian inference for multiple Gaussian graphical models with application to metabolic association networks
Efficient Bayesian regularization for graphical model selection
Markov chains for exploring posterior distributions. (With discussion)
Bayes factors for zero partial covariances
Efficient Gaussian graphical model determination under \(G\)-Wishart prior distributions
Hierarchical Gaussian graphical models: beyond reversible jump
Sparse permutation invariant covariance estimation
High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
Bayesian Lasso with neighborhood regression method for Gaussian graphical model
Unbiased approximation of posteriors via coupled particle Markov chain Monte Carlo
Network exploration via the adaptive LASSO and SCAD penalties
Quasi-Bayesian estimation of large Gaussian graphical models
Experiments in stochastic computation for high-dimensional graphical models
High-dimensional graphs and variable selection with the Lasso
Bayesian graphical Lasso models and efficient posterior computation
Joint High‐Dimensional Bayesian Variable and Covariance Selection with an Application to eQTL Analysis
Sparse Matrix Inversion with Scaled Lasso
Bayesian Inference for General Gaussian Graphical Models With Application to Multivariate Lattice Data
An Expectation Conditional Maximization Approach for Gaussian Graphical Models
Model selection and estimation in the Gaussian graphical model
The Bayesian Lasso
Determinant Maximization with Linear Matrix Inequality Constraints
Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models
Learning gene regulatory networks from next generation sequencing data
Handbook of Graphical Models
Graphical Gaussian Models With Edge and Vertex Symmetries
Reversible Jump, Birth-and-Death and More General Continuous Time Markov Chain Monte Carlo Samplers
Decomposable graphical Gaussian model determination
Bayesian model selection approach for coloured graphical Gaussian models
The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models
Bayesian Variational Inference for Exponential Random Graph Models
Fast Bayesian inference in large Gaussian graphical models
Bayesian Regularization for Graphical Models With Unequal Shrinkage
Exact estimation for Markov chain equilibrium expectations
Bayesian Variable Selection in Structured High-Dimensional Covariate Spaces With Applications in Genomics
Bayesian Subset Modeling for High-Dimensional Generalized Linear Models
Bayesian Inference of Multiple Gaussian Graphical Models
The huge Package for High-dimensional Undirected Graph Estimation in R
Simulation of hyper-inverse Wishart distributions in graphical models
A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models
Stochastic Gradient Markov Chain Monte Carlo
Markov Neighborhood Regression for High-Dimensional Inference
An Invitation to Sequential Monte Carlo Samplers
Efficient local updates for undirected graphical models
The Graphical Horseshoe Estimator for Inverse Covariance Matrices
Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models
Partial correlation graphical LASSO
Precision matrix estimation under the horseshoe-like prior-penalty dual
Bayesian sparse graphical models and their mixtures
Efficient sampling of Gaussian graphical models using conditional Bayes factors
A direct sampler for G-Wishart variates
Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models

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