Information Thresholds for Non-Parametric Structure Learning on Tree Graphical Models

We provide high probability finite sample complexity guarantees for non-parametric structure learning of tree-shaped graphical models whose nodes are discrete random variables with either finite or countable alphabets, both in the noiseless and noisy regimes. We study a fundamental quantity called the (noisy) information threshold, which arises naturally from the... Show more