On the maximum likelihood degree of Gaussian graphical models
Abstract
Abstract In this paper we revisit the likelihood geometry of Gaussian graphical models. We give a detailed proof that the ML‐degree behaves monotonically on induced subgraphs. Furthermore, we complete a missing argument that the ML‐degree of the cycle is larger than 1 for any , therefore completing the characterization that the only Gaussian graphical models with rational maximum likelihood estimator are the ones corresponding to chordal (decomposable) graphs. Finally, we prove that the formula for the ML‐degree of a cycle conjectured by Drton, Sturmfels and Sullivant provides a correct lower bound.
Fields of Study & Subjects
Bayesian Modeling and Causal Inference
Publication details
- Journal / source
- Bulletin of the London Mathematical Society
- Volume
- 58
- Issue
- 10
- Publication date
- 21 September 2026
- Language
- EN