Brain stroke lesion segmentation is of great importance for stroke rehabilitation
neuroimaging analysis. Due to the large variance of stroke lesion shapes and similarities of …

We consider the problem of learning a Gaussian graphical model in the case where the
observations come from two dependent groups sharing the same variables. We focus on a …

In this research paper, we explored the predictive capabilities of three different models of
Polynomial Regression (PR), Extreme Gradient Boosting (XGB), and LASSO to estimate the …

Accurate segmentation of infarct tissue in ischemic stroke is essential to determine the extent
of injury and assess the risk and choose optimal treatment for this life‐threatening disease …

Gaussian graphical models are nowadays commonly applied to the comparison of groups
sharing the same variables, by jointly learning their independence structures. We consider …

We consider the problem of learning a graphical model when the observations come from
two groups sharing the same variables but, unlike the usual approach to the joint learning of …

Complex networked systems driven by latent inputs are common in fields like neuroscience,
finance, and engineering. A key inference problem here is to learn edge connectivity from …

Supplement contains proofs and examples. We provide proofs of Theorems 1, 5, 6 along
with a background on representation theory that is needed to understand proofs. Moreover …

The study of hidden structures in data presents challenges in modern statistics and machine
learning. We introduce the $\mathbf {gips} $ package in R, which identifies permutation …

We present a novel method for inferring connectivity from large-scale neuronal networks
with synchronous activity. Our approach leverages Dynamic Differential Covariance to …