Bayesian Graphical Regression. Academic Article uri icon


  • We consider the problem of modeling conditional independence structures in heterogeneous data in the presence of additional subject-level covariates - termed Graphical Regression. We propose a novel specification of a conditional (in)dependence function of covariates - which allows the structure of a directed graph to vary flexibly with the covariates; imposes sparsity in both edge and covariate selection; produces both subject-specific and predictive graphs; and is computationally tractable. We provide theoretical justifications of our modeling endeavor, in terms of graphical model selection consistency. We demonstrate the performance of our method through rigorous simulation studies. We illustrate our approach in a cancer genomics-based precision medicine paradigm, where-in we explore gene regulatory networks in multiple myeloma taking prognostic clinical factors into account to obtain both population-level and subject-level gene regulatory networks.

published proceedings

  • J Am Stat Assoc

author list (cited authors)

  • Ni, Y., Stingo, F. C., & Baladandayuthapani, V.

citation count

  • 29

complete list of authors

  • Ni, Yang||Stingo, Francesco C||Baladandayuthapani, Veerabhadran

publication date

  • January 2019