Graphical models are powerful tools that are regularly used to investigate complex
dependence structures in high-throughput biomedical datasets. They allow for holistic …

In a traditional Gaussian graphical model, data homogeneity is routinely assumed with no
extra variables affecting the conditional independence. In modern genomic datasets, there is …

We consider the problem of modeling conditional independence structures in heterogenous
data in the presence of additional subject-level covariates—termed graphical regression …

We study the problem of estimating a temporally varying coefficient and varying structure
(VCVS) graphical model underlying data collected over a period of time, such as social …

Probabilistic graphical models provide a flexible yet parsimonious framework for modeling
dependencies among nodes in networks. There is a vast literature on parameter estimation …

This paper presents a Bayesian reformulation of covariate-assisted principal regression for
covariance matrix outcomes to identify low-dimensional components in the covariance …

We introduce Bayesian Gaussian graphical models with covariates (GGMx), a class of
multivariate Gaussian distributions with covariate-dependent sparse precision matrix. We …

Though Gaussian graphical models have been widely used in many scientific fields,
relatively limited progress has been made to link graph structures to external covariates. We …

It is well established that interpatient heterogeneity in cancer may significantly affect
genomic data analyses and in particular, network topologies. Most existing graphical model …

Sparse machine learning has recently emerged as powerful tool to obtain models of high-
dimensional data with high degree of interpretability, at low computational cost. This paper …