Joint Gaussian graphical model estimation: A survey
Joint Gaussian graphical model estimation: A survey
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DOI：
10.1002/wics.1582
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Katherine Tsai;Oluwasanmi Koyejo;M. Kolar
中科院分区：
文献类型：
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作者：
Katherine Tsai;Oluwasanmi Koyejo;M. Kolar
表示复杂系统的图通常在跨域中共享部分底层结构，同时保留各个特征。因此，识别共同结构可以揭示潜在的信号，例如，当应用于科学发现或临床诊断时。此外，越来越多的证据表明，跨域的共享结构提高了图的估计能力，特别是对于高维数据。然而，构建一个联合估计器来提取公共结构可能比看起来更复杂，这通常是由于数据源之间的数据异质性。这篇手稿调查了联合高斯图形模型的统计推断的最新工作，确定适合各种数据生成过程的模型结构。
Graphs representing complex systems often share a partial underlying structure across domains while retaining individual features. Thus, identifying common structures can shed light on the underlying signal, for instance, when applied to scientific discovery or clinical diagnoses. Furthermore, growing evidence shows that the shared structure across domains boosts the estimation power of graphs, particularly for high‐dimensional data. However, building a joint estimator to extract the common structure may be more complicated than it seems, most often due to data heterogeneity across sources. This manuscript surveys recent work on statistical inference of joint Gaussian graphical models, identifying model structures that fit various data generation processes.