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

Gaussian graphical models provide a powerful framework to reveal the conditional
dependency structure between multivariate variables. The process of uncovering the …

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

In this paper, we propose a Bayesian hierarchical approach to infer network structures
across multiple sample groups where both shared and differential edges may exist across …

We propose a Bayesian approximate inference method for learning the dependence
structure of a Gaussian graphical model. Using pseudo-likelihood, we derive an analytical …

Multi-dimensional data constituted by measurements along multiple axes have emerged
across many scientific areas such as genomics and cancer surveillance. A common …

Our work is motivated by the search for metabolite quantitative trait loci (QTL) in a cohort of
more than 5000 people. There are 158 metabolites measured by NMR spectroscopy in the …

Gaussian graphical models are useful tools for conditional independence structure
inference of multivariate random variables. Unfortunately, Bayesian inference of latent graph …

We present a sequential sampling methodology for weakly structural Markov laws, arising
naturally in a Bayesian structure learning context for decomposable graphical models. As a …

Decomposable graphs are known for their tedious and complicated Markov update steps.
Instead of modelling them directly, this work introduces a class of tree-dependent bipartite …