ArticleJournal of the American Statistical Association2015
Bayesian Inference of Multiple Gaussian Graphical Models.
Article in Journal of the American Statistical Association, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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Who cites it
59 citing papers in PubMed.
- NExON-Bayes: a Bayesian approach to network estimation informed by ordinal covariates.Bioinformatics (Oxford, England) · 2026Article
- The relationship between harsh parenting and adolescent cyberbullying: a network analysis.BMC psychology · 2026Article
- Integrative Analysis of Multimodal Omics Data.Annual review of statistics and its application · 2026Article
- Article
- Bayesian estimation of covariate assisted principal regression for brain functional connectivity.Biostatistics (Oxford, England) · 2025Article
- Connectivity Regression.Biostatistics (Oxford, England) · 2025Article
- A modeling framework for detecting and leveraging node-level information in Bayesian network inference.Biostatistics (Oxford, England) · 2025Article
- Simultaneous clustering and estimation of networks in multiple graphical models.Biostatistics (Oxford, England) · 2025Article
- Jointly estimating individual and group networks from fMRI data.Network neuroscience (Cambridge, Mass.) · 2025Article
- NetMIM: network-based multi-omics integration with block missingness for biomarker selection and disease outcome prediction.Briefings in bioinformatics · 2024Article
- Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing.Statistics in medicine · 2024Article
- Learning Bayesian Networks: A Copula Approach for Mixed-Type Data.Psychometrika · 2024Article
- The cluster D-trace loss for differential network analysis.Journal of applied statistics · 2024Article
- Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data.Journal of the American Statistical Association · 2024Article
- Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases.Journal of applied statistics · 2024Article
- Structure learning for gene regulatory networks.PLoS computational biology · 2023Article
- A Bayesian approach to differential edges with probabilistic interactions: applications in association and classification.Bioinformatics advances · 2023Article
- StabJGL: a stability approach to sparsity and similarity selection in multiple-network reconstruction.Bioinformatics advances · 2023Article
- NETWORK DIFFERENTIAL CONNECTIVITY ANALYSIS.The annals of applied statistics · 2022Article
- Bayesian Covariate-Dependent Gaussian Graphical Models with Varying Structure.Journal of machine learning research : JMLR · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In this paper, we propose a Bayesian approach to inference on multiple Gaussian graphical models. Specifically, we address the problem of inferring multiple undirected networks in situations where some of the networks may be unrelated, while others share common features. We link the estimation of the graph structures via a Markov random field (MRF) prior which encourages common edges. We learn which sample groups have a shared graph structure by placing a spike-and-slab prior on the parameters that measure network relatedness. This approach allows us to share information between sample groups, when appropriate, as well as to obtain a measure of relative network similarity across groups. Our modeling framework incorporates relevant prior knowledge through an edge-specific informative prior and can encourage similarity to an established network. Through simulations, we demonstrate the utility of our method in summarizing relative network similarity and compare its performance against related methods. We find improved accuracy of network estimation, particularly when the sample sizes within each subgroup are moderate. We also illustrate the application of our model to infer protein networks for various cancer subtypes and under different experimental conditions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.