Bayesian learning of graph substructures

DOI10.1214/22-ba1338arXiv2203.11664OpenAlexW4305054127MaRDI QIDQ6122075

Willem van den Boom, Alexandros Beskos, Maria De Iorio

Publication date: 27 February 2024

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2203.11664

zbMATH Keywords

multivariate data analysisBayesian nonparametricsGaussian graphical modelsdependent Dirichlet processmultiple graphical modelsdegree-corrected stochastic blockmodels

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Sparse inverse covariance estimation with the graphical lasso
• Bayesian graphical models for differential pathways
• Bayesian inference for multiple Gaussian graphical models with application to metabolic association networks
• Optimal predictive model selection.
• Inferring sparse Gaussian graphical models with latent structure
• Minimax rates in network analysis: graphon estimation, community detection and hypothesis testing
• Bayesian inference for high-dimensional decomposable graphs
• The dependent Dirichlet process and related models
• Extended stochastic block models with application to criminal networks
• Bayesian testing for exogenous partition structures in stochastic block models
• Bayesian graphical models for modern biological applications
• A random effects stochastic block model for joint community detection in multiple networks with applications to neuroimaging
• Bayesian estimation of the latent dimension and communities in stochastic blockmodels
• Bayesian graph selection consistency under model misspecification
• A general framework for Bayes structured linear models
• Latent nested nonparametric priors (with discussion)
• Combinatorial stochastic processes. Ecole d'Eté de Probabilités de Saint-Flour XXXII -- 2002.
• A Bayesian analysis of some nonparametric problems
• Bayesian clustering in decomposable graphs
• Hierarchical Dirichlet Processes
• Bayesian cluster analysis
• Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models
• Marginal Likelihood and Bayes Factors for Dirichlet Process Mixture Models
• Community Detection and Stochastic Block Models
• Handbook of Graphical Models
• Sparse Graphs Using Exchangeable Random Measures
• Mixture Models With a Prior on the Number of Components
• A Method for Combining Inference Across Related Nonparametric Bayesian Models
• Modeling Protein Expression and Protein Signaling Pathways
• The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models
• Dependent Modeling of Temporal Sequences of Random Partitions
• Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities
• Dynamic degree-corrected blockmodels for social networks: A nonparametric approach
• The Weighted Likelihood Ratio, Linear Hypotheses on Normal Location Parameters
• Probabilistic Community Detection With Unknown Number of Communities
• Statistical Clustering of Temporal Networks Through a Dynamic Stochastic Block Model
• Bayesian Inference of Multiple Gaussian Graphical Models
• High-dimensional covariance estimation based on Gaussian graphical models
• An ANOVA Model for Dependent Random Measures
• A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models
• Random Partition Models for Microclustering Tasks
• Networks
• A Bayesian nonparametric latent space approach to modeling evolving communities in dynamic networks