3 papers
stat.ME2026
Parameterising Gaussian Graphical Models
Jack Storror Carter
Gaussian graphical models (GGMs) describe the dependence structure among jointly Gaussian random variables. However, the most common parameterisation of GGMs, the precision matrix,…
stat.ME2026
Positive-definiteness in separable priors: effects on prior interpretability and inference
Jack Storror Carter, David Rossell
A popular class of priors for symmetric positive-definite matrices assumes independent entries and adds a truncation to ensure positive-definiteness. While conceptually simple and…
stat.ME2025
Existence and optimisation of the partial correlation graphical lasso
Jack Storror Carter, Cesare Molinari
The partial correlation graphical LASSO (PCGLASSO) is a penalised likelihood method for Gaussian graphical models which provides scale invariant sparse estimation of the precision…