In simulations, the Bayesian GLASSO performed better than the Horseshoe prior for estimating symptom networks¶
Claim · All claims
Evidence · 1 study · causalr2·q1
Currency · newest study 2022 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2025: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q1 i? Results showed that the Bayesian GLASSO performed better than the Horseshoe prior in the simulation comparison of symptom-network estimation. → Joran Jongerling 2022
Evidence¶
Joran Jongerling 2022¶
Joran Jongerling, Sacha Epskamp, Donald Williams. (2022). Bayesian uncertainty estimation for Gaussian graphical models and centrality indices. Multivariate Behavioral Research. https://www.nwea.org/research/publication/bayesian-uncertainty-estimation-for-gaussian-graphical-models-and-centrality-indices/
q1 · i? · causal · r2
Simulation comparison of Bayesian GLASSO- and Horseshoe priors: "the Bayesian GLASSO performed better than the Horseshoe". The available text prints no effect sizes or specific measures for this contrast.
"Results showed that the Bayesian GLASSO performed better than the Horseshoe, and that the Bayesian GLASSO outperformed the frequentist GLASSO"
Discussion¶
Related Claims¶
- In simulations, Bayesian GLASSO estimation outperforms frequentist GLASSO on bias in edge weights, centrality measures, correlation between estimated and true partial correlations, and specificity — related
- Sensitivity is better for the frequentist GLASSO than for the Bayesian GLASSO, though Bayesian GLASSO performance is usually close — related
- The frequentist graphical LASSO has difficulty estimating centrality indices and uncertainty in these measures in symptom networks — related
- Bayesian GLASSO shows good coverage of uncertainty for strength and closeness centrality, but uncertainty in betweenness centrality is estimated less well — related
- Changes in ICT career aspirations are not systematically linked to contemporaneous changes in other occupational domains — related
- Regularization-based graphical modeling has key drawbacks, including difficulty obtaining valid parameter uncertainty and inflated false positive rates — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
bayesian-glasso-beats-horseshoe-prior |
title |
In simulations, the Bayesian GLASSO performed better than the Horseshoe prior for estimating symptom networks |
description |
In simulations, the Bayesian GLASSO performed better than the Horseshoe prior for estimating symptom networks |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2022 |
recheck_by |
2025 |
generated |
by process:wiki-ingest on 2026-10-08 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — joran-jongerling-2022 |
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