The goal of this library is to solve the problem of calculating the inverse of a symmetric positive definite matrix (e.g. a covariance matrix) when the constraints are mixed between the covariance ...

the goal is to find the elements of P.In other words, every unique element (i,j) of the n x n symmetric matrix has a given constraint, either to a value in the covariance matrix, or a zero entry in ...

This paper proposes a penalized composite likelihood method for model selection in colored graphical Gaussian models. The method provides a sparse and symmetry-constrained estimator of the precision ...

This paper proposes a model which approximates full covariance matrices in Gaussian mixture models (GMM) with a reduced number of parameters and computations required for likelihood evaluations. In ...

Gaussian Graphical Model. Consider the model on a zero-mean Gaussian distribution where $\Sigma^{(n)}$ encode dependencies among variables. As we mentioned before, the inverted matrix of the ...

Abstract: We address the task of estimating sparse structured precision matrices for multivariate Gaussian random variables within a graphical model framework. We propose two novel estimators based on ...

Precision Matrix: The inverse of the covariance matrix, which provides insights into the conditional independence among variables. Graphical Models and Covariance Matrix Estimation Publication Trend ...

Nearest neighbors model of order k, denoted NN(k) and described in 18 18 H. Li & J. Gui. Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of ...

Graphical models provide a robust framework for representing the conditional independence structure between variables through networks, enabling nuanced insight into complex high-dimensional data.