On a conjecture of Roverato regarding G-Wishart normalising constants
Ching Wong, Giusi Moffa, Jack Kuipers
TL;DR
This paper investigates Roverato's conjecture that the G-Wishart normalising constant $\mathscr C_\mathcal{G}(\delta,D)$ can be rewritten using identity-scale constants. It recasts the conjecture in an equivalent form via the PD-completion $D^{\mathcal G}$ and the Isserlis matrix $\mathrm{Iss}_{\mathcal G}(D)$, and analyzes a one-edge deletion setup to derive a gamma-function based ratio that the conjecture would force. A concrete counterexample (a 4-cycle vs a 3-edge path) demonstrates that the conjectured equality fails in general, though a related ratio approximation remains a good leading-order estimate, especially for large $\delta$ or data size. The work clarifies the limits of Roverato's conjecture and highlights the practical utility of the induced ratio approximation in Bayesian computation, while leaving open questions about higher-order corrections and data-invariant closed forms.
Abstract
The evaluation of G-Wishart normalising constants is a core component for Bayesian analyses for Gaussian graphical models, but remains a computationally intensive task in general. Based on empirical evidence, Roverato [Scandinavian Journal of Statistics, 29:391--411 (2002)] observed and conjectured that such constants can be simplified and rewritten in terms of constants with an identity scale matrix. In this note, we disprove this conjecture for general graphs by showing that the conjecture instead implies an independently-derived approximation for certain ratios of normalising constants.
