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Bibliographic Details
Main Authors: Wong, Ching, Moffa, Giusi, Kuipers, Jack
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2503.13046
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Table of Contents:
  • 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.