Concentration of a sparse Bayesian model with Horseshoe prior in estimating high-dimensional precision matrix

arXiv:2406.14269v1 Announce Type: cross
Abstract: Precision matrices are crucial in many fields such as social networks, neuroscience, and economics, representing the edge structure of Gaussian graphical models (GGMs), where a zero in an off-diagonal position of the precision matrix indicates conditional independence between nodes. In high-dimensional settings where the dimension of the precision matrix $p$ exceeds the sample size $n$ and the matrix is sparse, methods like graphical Lasso, graphical SCAD, and CLIME are popular for estimating GGMs. While frequentist …

abstract arxiv bayesian economics edge fields math.st matrix networks neuroscience nodes off precision prior social social networks stat.ml stat.th the edge type