Search Results for author:
Found 3 papers, 1 papers with code
Algorithmic Bayesian Group Gibbs Selection
Bayesian model selection, with precedents in George and McCulloch (1993) and Abramovich et al. (1998), support credibility measures that relate model uncertainty, but computation can be costly when sparse priors are approximate.
Computation Methodology
Joint Estimation of Multiple Dependent Gaussian Graphical Models with Applications to Mouse Genomics
In this paper, we propose a novel estimator for data arising from a group of Gaussian graphical models that are themselves dependent.
A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection
We describe a simple, efficient, permutation based procedure for selecting the penalty parameter in the LASSO.
