ArticleProceedings of machine learning research2019
Bayesian Joint Spike-and-Slab Graphical Lasso.
Article in Proceedings of machine learning research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 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
- A probabilistic modeling framework for genomic networks incorporating sample heterogeneity.Cell reports methods · 2025Article
- StabJGL: a stability approach to sparsity and similarity selection in multiple-network reconstruction.Bioinformatics advances · 2023Article
- Integration of immune and hypoxia gene signatures improves the prediction of radiosensitivity in breast cancer.American journal of cancer research · 2022Article
- Bayesian hierarchical factor regression models to infer cause of death from verbal autopsy data.Journal of the Royal Statistical Society. Series C, Applied statistics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
In this article, we propose a new class of priors for Bayesian inference with multiple Gaussian graphical models. We introduce Bayesian treatments of two popular procedures, the group graphical lasso and the fused graphical lasso, and extend them to a continuous spike-and-slab framework to allow self-adaptive shrinkage and model selection simultaneously. We develop an EM algorithm that performs fast and dynamic explorations of posterior modes. Our approach selects sparse models efficiently and automatically with substantially smaller bias than would be induced by alternative regularization procedures. The performance of the proposed methods are demonstrated through simulation and two real data examples.
Identifiers
33521648PMC7845917What OpenQuestion holds
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.