11 citations · 23 across the 6 of their papers we have counts for
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stat.ME2018★ 2 cited
Regression-Based Bayesian Estimation and Structure Learning for Nonparanormal Graphical Models
Jami J. Mulgrave, Subhashis Ghosal
A nonparanormal graphical model is a semiparametric generalization of a Gaussian graphical model for continuous variables in which it is assumed that the variables follow a Gaussia…
stat.ME2018
Bayesian Analysis of Nonparanormal Graphical Models Using Rank-Likelihood
Jami J. Mulgrave, Subhashis Ghosal
Gaussian graphical models, where it is assumed that the variables of interest jointly follow a multivariate normal distribution with a sparse precision matrix, have been used to st…
stat.ME2018★ 11 cited
Bayesian Inference in Nonparanormal Graphical Models
Jami J. Mulgrave, Subhashis Ghosal
Gaussian graphical models have been used to study intrinsic dependence among several variables, but the Gaussianity assumption may be restrictive in many applications. A nonparanor…