Exact formulas for the normalizing constants of Wishart distributions for graphical models

TL;DR

This paper derives explicit formulas for the normalizing constants of G-Wishart distributions in Gaussian graphical models, extending known results from chordal graphs to general graphs, thereby resolving a longstanding open problem.

Contribution

It provides the first explicit formulas for the normalizing constants of G-Wishart distributions for arbitrary graphs, advancing Bayesian analysis in graphical models.

Findings

Explicit formulas for general graph normalizing constants

Resolution of a long-standing open problem

Facilitates Bayesian inference in non-chordal graphs

Abstract

Gaussian graphical models have received considerable attention during the past four decades from the statistical and machine learning communities. In Bayesian treatments of this model, the G-Wishart distribution serves as the conjugate prior for inverse covariance matrices satisfying graphical constraints. While it is straightforward to posit the unnormalized densities, the normalizing constants of these distributions have been known only for graphs that are chordal, or decomposable. Up until now, it was unknown whether the normalizing constant for a general graph could be represented explicitly, and a considerable body of computational literature emerged that attempted to avoid this apparent intractability. We close this question by providing an explicit representation of the G-Wishart normalizing constant for general graphs.