A graphical model is a statistical model that is associated with a graph whose nodes
correspond to variables of interest. The edges of the graph reflect allowed conditional …

Network analysis is entering fields where network structures are unknown, such as
psychology and the educational sciences. A crucial step in the application of network …

We propose a semiparametric approach called the nonparanormal SKEPTIC for efficiently
and robustly estimating high-dimensional undirected graphical models. To achieve …

Recent methods for estimating sparse undirected graphs for real-valued data in high
dimensional problems rely heavily on the assumption of normality. We show how to use a …

Regularized rank-based estimation of high-dimensional nonparanormal graphical models Page
1 The Annals of Statistics 2012, Vol. 40, No. 5, 2541–2571 DOI: 10.1214/12-AOS1041 © Institute …

Many investigations into the world, including philosophical ones, aim to discover causal
knowledge, and many experimental methods have been developed to assist in causal …

Graphical models in their modern form have been around since the late 1970s and appear
today in many areas of the sciences. Along with the ongoing developments of graphical …

Covariance matrix and its inverse, known as the precision matrix, have many applications in
multivariate analysis because their elements can exhibit the variance, correlation …

We propose a new procedure for optimally estimating high dimensional Gaussian graphical
models. Our approach is asymptotically tuning-free and non-asymptotically tuning …

We consider the problem of learning a Bayesian network or directed acyclic graph model
from observational data. A number of constraint‐based, score‐based and hybrid algorithms …