Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Iterative conditional fitting for Gaussian ancestral graph models
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Convolutional factor graphs as probabilistic models
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models
The Journal of Machine Learning Research
Reading dependencies from covariance graphs
International Journal of Approximate Reasoning
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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 algorithms.