Bayesian Structure Learning in Graphical Models using Shrinkage priors
Statistics Theory
2019-08-08 v1 Methodology
Statistics Theory
Abstract
We consider the problem of learning the structure of a high dimensional precision matrix under sparsity assumptions. We propose to use a shrinkage prior, called the DL-graphical prior based on the Dirichlet-Laplace prior used for the Gaussian mean problem. A posterior sampling scheme based on Gibbs sampling is also provided along with theoretical guarantees of the method by obtaining the posterior convergence rate of the precision matrix.
Cite
@article{arxiv.1908.02684,
title = {Bayesian Structure Learning in Graphical Models using Shrinkage priors},
author = {Sayantan Banerjee},
journal= {arXiv preprint arXiv:1908.02684},
year = {2019}
}
Comments
This is an extended abstract version of the ongoing work