On Graphical Models and Convex Geometry
Methodology
2021-06-29 v1
Abstract
We introduce a mixture-model of beta distributions to identify significant correlations among predictors when is large. The method relies on theorems in convex geometry, which we use to show how to control the error rate of edge detection in graphical models. Our `betaMix' method does not require any assumptions about the network structure, nor does it assume that the network is sparse. The results in this article hold for a wide class of data generating distributions that include light-tailed and heavy-tailed spherically symmetric distributions.
Cite
@article{arxiv.2106.14255,
title = {On Graphical Models and Convex Geometry},
author = {Haim Bar and Martin T. Wells},
journal= {arXiv preprint arXiv:2106.14255},
year = {2021}
}