ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2019
An Expectation Conditional Maximization approach for Gaussian graphical models.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- NExON-Bayes: a Bayesian approach to network estimation informed by ordinal covariates.Bioinformatics (Oxford, England) · 2026Article
- A modeling framework for detecting and leveraging node-level information in Bayesian network inference.Biostatistics (Oxford, England) · 2025Article
- Regression-Based Bayesian Estimation and Structure Learning for Nonparanormal Graphical Models.Statistical analysis and data mining · 2022Article
- Latent Network Estimation and Variable Selection for Compositional Data Via Variational EM.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2022Article
- Bayesian modeling of multiple structural connectivity networks during the progression of Alzheimer's disease.Biometrics · 2020Article
- Using Bayesian Latent Gaussian Graphical Models to Infer Symptom Associations in Verbal Autopsies.Bayesian analysis · 2020Article
- Bayesian Joint Spike-and-Slab Graphical Lasso.Proceedings of machine learning research · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Bayesian graphical models are a useful tool for understanding dependence relationships among many variables, particularly in situations with external prior information. In high-dimensional settings, the space of possible graphs becomes enormous, rendering even state-of-the-art Bayesian stochastic search computationally infeasible. We propose a deterministic alternative to estimate Gaussian and Gaussian copula graphical models using an Expectation Conditional Maximization (ECM) algorithm, extending the EM approach from Bayesian variable selection to graphical model estimation. We show that the ECM approach enables fast posterior exploration under a sequence of mixture priors, and can incorporate multiple sources of information.
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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.