On some algorithms for estimation in Gaussian graphical models

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

In Gaussian graphical models, the likelihood equations must typically be solved iteratively. We investigate two algorithms: A version of iterative proportional scaling which avoids inversion of large matrices, and an algorithm based on convex duality and operating on the covariance matrix by neighbourhood coordinate descent, corresponding to the graphical lasso with zero penalty. For large, sparse graphs, the iterative proportional scaling algorithm appears feasible and has simple convergence properties. The algorithm based on neighbourhood coordinate descent is extremely fast and less dependent on sparsity, but needs a positive definite starting value to converge. We give an algorithm for finding such a starting value for graphs with low colouring number. As a consequence, we also obtain a simplified proof for existence of the maximum likelihood estimator in such cases.
OriginalsprogEngelsk
TidsskriftBiometrika
ISSN0006-3444
DOI
StatusE-pub ahead of print - 2024

ID: 395024443