A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence
This work addresses a specific problem in statistical modeling for researchers in graphical models, but it appears incremental as it builds on existing techniques without introducing a new paradigm.
The paper tackles maximum likelihood estimation in Gaussian graphical models for marginal independence by presenting a new fitting algorithm that uses standard regression techniques and establishes its convergence properties, contrasting it with existing methods.
Graphical models with bi-directed edges (<->) represent marginal independence: the absence of an edge between two vertices indicates that the corresponding variables are marginally independent. In this paper, we consider maximum likelihood estimation in the case of continuous variables with a Gaussian joint distribution, sometimes termed a covariance graph model. We present a new fitting algorithm which exploits standard regression techniques and establish its convergence properties. Moreover, we contrast our procedure to existing estimation methods.