Bayesian inference of hub nodes across multiple networks
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DOI: 10.1111/biom.12958
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References listed on IDEAS
- Aliye Atay-Kayis & Helène Massam, 2005. "A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models," Biometrika, Biometrika Trust, vol. 92(2), pages 317-335, June.
- Alberto Roverato, 2002. "Hyper Inverse Wishart Distribution for Non‐decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(3), pages 391-411, September.
- Patrick Danaher & Pei Wang & Daniela M. Witten, 2014. "The joint graphical lasso for inverse covariance estimation across multiple classes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(2), pages 373-397, March.
- Christine Peterson & Francesco C. Stingo & Marina Vannucci, 2015. "Bayesian Inference of Multiple Gaussian Graphical Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 159-174, March.
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- Yang Ni & Veerabhadran Baladandayuthapani & Marina Vannucci & Francesco C. Stingo, 2022. "Rejoinder to the discussion of “Bayesian graphical models for modern biological applications”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 287-294, June.
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