Comment on Vogels et al.'s (2024) "Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison": An updated performance check using BGGM
Abstract
Vogels et al. (2024) presented an empirical comparison of Bayesian methods for structure learning in undirected Gaussian graphical models. The method implemented in the R package BGGM was run with equal prior probabilities for a null, negative, and positive partial correlation, implying a prior inclusion probability (equivalent to a prior graph density) of 2/3. The other Bayesian methods in the comparison used a prior graph density of 0.2 however. The data-generating densities ranged from 0.01 to 0.1. This resulted in a substantial overestimation of the inclusion probabilities of absent edges by BGGM. For a fair comparison of the performance of the different methods, I repeated the simulation using a prior inclusion probability of 0.2 using BGGM. In this case, the performance of BGGM is comparable with the other Bayesian methods.
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BibTeX
@misc{mulder2026comment,
title = {{Comment on Vogels et al.'s (2024) "Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison": An updated performance check using BGGM}},
author = {Joris Mulder},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.31969},
eprint = {2609.31969},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.31969},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - Comment on Vogels et al.'s (2024) "Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison": An updated performance check using BGGM
AU - Joris Mulder
PY - 2026
DA - 2026/09/25
PB - arXiv
DO - 10.48550/arxiv.2609.31969
UR - https://arxiv.org/abs/2609.31969
AB - Vogels et al. (2024) presented an empirical comparison of Bayesian methods for structure learning in undirected Gaussian graphical models. The method implemented in the R package BGGM was run with equal prior probabilities for a null, negative, and positive partial correlation, implying a prior inclusion probability (equivalent to a prior graph density) of 2/3. The other Bayesian methods in the comparison used a prior graph density of 0.2 however. The data-generating densities ranged from 0.01 to 0.1. This resulted in a substantial overestimation of the inclusion probabilities of absent edges by BGGM. For a fair comparison of the performance of the different methods, I repeated the simulation using a prior inclusion probability of 0.2 using BGGM. In this case, the performance of BGGM is comparable with the other Bayesian methods.
ER -