113 citations · 316 across the 21 of their papers we have counts for
23 papers
The scalable Birth-Death MCMC Algorithm for Mixed Graphical Model Learning with Application to Genomic Data Integration
Nanwei Wang, Laurent Briollais, Helene Massam
Recent advances in biological research have seen the emergence of high-throughput technologies with numerous applications that allow the study of biological mechanisms at an unprec…
Bayesian model selection approach for colored graphical Gaussian models
Qiong Li, Xin Gao, Helene Massam
We consider a class of colored graphical Gaussian models obtained by placing symmetry constraints on the precision matrix in a Bayesian framework. The prior distribution on the pre…
Model selection in the space of Gaussian models invariant by symmetry
Piotr Graczyk, Hideyuki Ishi, Bartosz Kołodziejek +1
We consider multivariate centered Gaussian models for the random variable , invariant under the action of a subgroup of the group of permutations on $\{1,\ldot…
Gaussian approximation of Gaussian scale mixture
Gérard Letac, Hélène Massam
For a given positive random variable and a given independent of , we compute the scalar such that the distance between and i…
Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models
Reza Mohammadi, Helene Massam, Gerard Letac
Gaussian graphical models are relevant tools to learn conditional independence structure between variables. In this class of models, Bayesian structure learning is often done by se…
Analyzing Genome-wide Association Study Data with the R Package genMOSS
Matthew Friedlander, Adrian Dobra, Helene Massam +1
The R package (R Core Team (2016)) genMOSS is specifically designed for the Bayesian analysis of genome-wide association study data. The package implements the mode oriented stocha…