A new algorithm for maximum likelihood estimation in Gaussian graphical models for marginal independence

A new algorithm for maximum likelihood estimation in Gaussian graphical models for marginal independence (2003)

by Mathias Drton

Venue:	In U. Kjærulff and C. Meek (Eds.), Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence
Citations:	12 - 6 self

Datum	Value	Source
TITLE	A new algorithm for maximum likelihood estimation in Gaussian graphical models for marginal independence	INFERENCE
AUTHOR NAME	Mathias Drton	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Statistics; University of Washington	SVM HeaderParse 0.2
AUTHOR ADDR	Seattle, WA 98195-4322	SVM HeaderParse 0.2
ABSTRACT	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. 1	SVM HeaderParse 0.2
YEAR	2003	INFERENCE
VENUE	In U. Kjærulff and C. Meek (Eds.), Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	184--191	INFERENCE
CITATIONS	15 found	ParsCit 1.0