Bayesian Structure Learning in Graphical Models using Shrinkage priors

Sayantan Banerjee

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arXiv · 1908.02684

Bayesian Structure Learning in Graphical Models using Shrinkage priors

Sayantan Banerjee

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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.

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Sayantan Banerjee. 2019-07-26. Bayesian Structure Learning in Graphical Models using Shrinkage priors. https://arxiv.org/abs/1908.02684

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