Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo
Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo
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DOI：
10.1007/s10260-021-00600-7
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发表时间：
2021-11
影响因子：
1
通讯作者：
M. Schweinberger
中科院分区：
文献类型：
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作者：
M. Schweinberger
很高兴祝贺Ni et al. （Stat Methods Appl 490:1 - 32,2021）在Ni et al. （Stat Methods Appl 490:1 - 32,2021）中综述的贝叶斯图形模型的最新进展。作者对捕获生物网络显著特征的贝叶斯图形模型的构建和估计给予了相当大的思考。我的讨论重点是计算的挑战和机遇以及先验，指出Ni等人回顾的马尔可夫随机场先验的局限性（Stat Methods Appl 490:1 - 32,2021），并探索捕获条件独立图的附加特征（如枢纽结构和聚类）的可能概括。最后，我简短地讨论了图形模型和随机图模型的交集。
It is a pleasure to congratulate Ni et al. (Stat Methods Appl 490:1–32, 2021) on the recent advances in Bayesian graphical models reviewed in Ni et al. (Stat Methods Appl 490:1–32, 2021). The authors have given considerable thought to the construction and estimation of Bayesian graphical models that capture salient features of biological networks. My discussion focuses on computational challenges and opportunities along with priors, pointing out limitations of the Markov random field priors reviewed in Ni et al. (Stat Methods Appl 490:1–32, 2021) and exploring possible generalizations that capture additional features of conditional independence graphs, such as hub structure and clustering. I conclude with a short discussion of the intersection of graphical models and random graph models.