Learning Block Structured Graphs in Gaussian Graphical Models
From MaRDI portal
Publication:6552531
DOI10.1080/10618600.2023.2210184MaRDI QIDQ6552531
Alessia Pini, Alessandro Colombi, Lucia Paci, Raffaele Argiento
Publication date: 10 June 2024
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Bayesian structure learning in sparse Gaussian graphical models
- Scaling it up: stochastic search structure learning in graphical models
- The max-min hill-climbing Bayesian network structure learning algorithm
- On the prior and posterior distributions used in graphical modelling
- Hyper Markov laws in the statistical analysis of decomposable graphical models
- Improving Markov chain Monte Carlo model search for data mining
- The cluster graphical Lasso for improved estimation of Gaussian graphical models
- Bayesian inference for multiple Gaussian graphical models with application to metabolic association networks
- Exact formulas for the normalizing constants of Wishart distributions for graphical models
- Optimal predictive model selection.
- Efficient Gaussian graphical model determination under \(G\)-Wishart prior distributions
- Inferring sparse Gaussian graphical models with latent structure
- Unbiased approximation of posteriors via coupled particle Markov chain Monte Carlo
- Gaussian graphical modeling for spectrometric data analysis
- Hierarchical normalized completely random measures for robust graphical modeling
- Experiments in stochastic computation for high-dimensional graphical models
- Bayesian Inference for General Gaussian Graphical Models With Application to Multivariate Lattice Data
- Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models
- Bayesian Graphical Models for Discrete Data
- Decomposable graphical Gaussian model determination
- A Unified Framework for Structured Graph Learning via Spectral Constraints
- The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models
- Informed Proposals for Local MCMC in Discrete Spaces
- Bayesian Inference of Multiple Gaussian Graphical Models
- A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models
- A direct sampler for G-Wishart variates
- Bayesian learning of multiple directed networks from observational data
This page was built for publication: Learning Block Structured Graphs in Gaussian Graphical Models
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6552531)