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Inequalities on partial correlations in Gaussian graphical models containing star shapes

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  • Edmund Jones
  • Vanessa Didelez

Abstract

This short paper proves inequalities that restrict the magnitudes of the partial correlations in star-shaped structures in Gaussian graphical models. These inequalities have to be satisfied by distributions that are used for generating simulated data to test structure-learning algorithms, but methods that have been used to create such distributions do not always ensure that they are. The inequalities are also noteworthy because stars are common and meaningful in real-world networks.

Suggested Citation

  • Edmund Jones & Vanessa Didelez, 2016. "Inequalities on partial correlations in Gaussian graphical models containing star shapes," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(20), pages 5990-5996, October.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:20:p:5990-5996
    DOI: 10.1080/03610926.2014.953696
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