Joint Gaussian graphical model estimation: A survey

Katherine Tsai, Oluwasanmi Koyejo, Mladen Kolar

Research output: Contribution to journalReview articlepeer-review

Abstract

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. This article is categorized under: Data: Types and Structure > Graph and Network Data Statistical Models > Graphical Models.

Original languageEnglish (US)
JournalWiley Interdisciplinary Reviews: Computational Statistics
DOIs
StateAccepted/In press - 2022

Keywords

  • Gaussian graphical model
  • graphical lasso
  • high-dimensional estimation
  • joint network
  • sparsity

ASJC Scopus subject areas

  • Statistics and Probability

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