4 papers
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,…
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…
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…
Existence of penalised likelihood estimates and posterior propriety of separable prior distributions for Gaussian precision matrices
Jack Storror Carter
Penalised likelihoods are often used for sparse estimation of a Gaussian precision matrix. In high dimensional settings where the matrix dimension is larger than the sample size, t…