Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
An Approximate Minimum Degree Ordering Algorithm
SIAM Journal on Matrix Analysis and Applications
An Interior-Point Method for Approximate Positive Semidefinite Completions
Computational Optimization and Applications
Determinant Maximization with Linear Matrix Inequality Constraints
SIAM Journal on Matrix Analysis and Applications
On computing certain elements of the inverse of a sparse matrix
Communications of the ACM
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Exploiting Sparsity in Semidefinite Programming via Matrix Completion I: General Framework
SIAM Journal on Optimization
Convex Optimization
The Journal of Machine Learning Research
Sparse Gaussian graphical models with unknown block structure
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive First-Order Methods for General Sparse Inverse Covariance Selection
SIAM Journal on Matrix Analysis and Applications
Network-scale traffic modeling and forecasting with graphical lasso
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Solving Log-Determinant Optimization Problems by a Newton-CG Primal Proximal Point Algorithm
SIAM Journal on Optimization
Model selection and estimation in the matrix normal graphical model
Journal of Multivariate Analysis
The cost of using decomposable Gaussian graphical models for computational convenience
Computational Statistics & Data Analysis
Fitting very large sparse Gaussian graphical models
Computational Statistics & Data Analysis
A joint convex penalty for inverse covariance matrix estimation
Computational Statistics & Data Analysis
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We describe algorithms for maximum likelihood estimation of Gaussian graphical models with conditional independence constraints. This problem is also known as covariance selection, and it can be expressed as an unconstrained convex optimization problem with a closed-form solution if the underlying graph is chordal. The focus of the paper is on iterative algorithms for covariance selection with nonchordal graphs. We first derive efficient methods for evaluating the gradient and Hessian of the log-likelihood function when the underlying graph is chordal. The algorithms are formulated as simple recursions on a clique tree associated with the graph. We also show that the gradient and Hessian mappings are easily inverted when the underlying graph is chordal. We then exploit these results to obtain efficient implementations of Newton's method and the conjugate gradient method for large nonchordal graphs, by embedding the graph in a chordal graph.