AI 中文总结
评论指出 Vogels 等人比较中 BGGM 因先验包含概率设置不当（2/3 对其他方法 0.2）导致性能被低估，改用 0.2 后 BGGM 性能与其他贝叶斯方法相当。
AI 中文摘要
Vogels 等人（2024）对无向高斯图模型中结构学习的贝叶斯方法进行了实证比较。R 包 BGGM 中实现的方法在运行时为零、负和正偏相关系数设置了相等的先验概率，这意味着先验包含概率（相当于先验图密度）为 2/3。然而，比较中的其他贝叶斯方法使用的先验图密度为 0.2。数据生成密度范围从 0.01 到 0.1。这导致 BGGM 对不存在边的包含概率产生了显著的过度估计。为了公平比较不同方法的性能，我使用 BGGM 以 0.2 的先验包含概率重复了模拟。在这种情况下，BGGM 的性能与其他贝叶斯方法相当。
英文摘要
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.
Commentscomment on arXiv:2307.02603v3; 10 pages, 3 tables
