The generalization of the classical multivariate t-distribution to the Dirichlet t-distribution proposed in the paper under discussion allows to model graphs that account for outliers, still keeping a reasonably low computational burden. In this comment we focus on a possible further generalization aiming at incorporating skewness in the analysis
AbstractA new Gaussian graphical modeling that is robustified against possible outliers is proposed....
In this paper, an alternative skew Student-t family of distributions is studied. It is obtained as a...
Introduction: A Dirichlet process (DP) is a distribution over probability distributions. We generall...
The generalization of the classical multivariate t-distribution to the Dirichlet t-distribution prop...
The paper we discuss provides both theoretical and computational results for robust structure estima...
Bayesian graphical modeling provides an appealing way to obtain uncertainty esti-mates when inferrin...
Graphical Gaussian models have proven to be useful tools for exploring network structures based on m...
We contribute to the discussion of the paper by Ni et al. (Stat Methods Appl, 2021. https://doi...
Gaussian graphical models are useful tools for exploring network structures in multivariate normal d...
The robustness problem is tackled by adopting a parametric class of distributions flexible enough ...
Graphical model learning and inference are often performed using Bayesian techniques. In particular,...
Theory of graphical models has matured over more than three decades to provide the backbone for seve...
Models based on multivariate t distributions are widely applied to analyze data with heavy tails. Ho...
In this study, two classes of multivariate distributions are proposed as extensions of the well kn...
The robustness problem is tackled by adopting a parametric class of distributions flexible enough to...
AbstractA new Gaussian graphical modeling that is robustified against possible outliers is proposed....
In this paper, an alternative skew Student-t family of distributions is studied. It is obtained as a...
Introduction: A Dirichlet process (DP) is a distribution over probability distributions. We generall...
The generalization of the classical multivariate t-distribution to the Dirichlet t-distribution prop...
The paper we discuss provides both theoretical and computational results for robust structure estima...
Bayesian graphical modeling provides an appealing way to obtain uncertainty esti-mates when inferrin...
Graphical Gaussian models have proven to be useful tools for exploring network structures based on m...
We contribute to the discussion of the paper by Ni et al. (Stat Methods Appl, 2021. https://doi...
Gaussian graphical models are useful tools for exploring network structures in multivariate normal d...
The robustness problem is tackled by adopting a parametric class of distributions flexible enough ...
Graphical model learning and inference are often performed using Bayesian techniques. In particular,...
Theory of graphical models has matured over more than three decades to provide the backbone for seve...
Models based on multivariate t distributions are widely applied to analyze data with heavy tails. Ho...
In this study, two classes of multivariate distributions are proposed as extensions of the well kn...
The robustness problem is tackled by adopting a parametric class of distributions flexible enough to...
AbstractA new Gaussian graphical modeling that is robustified against possible outliers is proposed....
In this paper, an alternative skew Student-t family of distributions is studied. It is obtained as a...
Introduction: A Dirichlet process (DP) is a distribution over probability distributions. We generall...