--- res: bibo_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.@eng' bibo_authorlist: - foaf_Person: foaf_givenName: Caroline foaf_name: Caroline Uhler foaf_surname: Uhler foaf_workInfoHomepage: http://www.librecat.org/personId=49ADD78E-F248-11E8-B48F-1D18A9856A87 orcid: 0000-0002-7008-0216 - foaf_Person: foaf_givenName: Alex foaf_name: Lenkoski, Alex foaf_surname: Lenkoski - foaf_Person: foaf_givenName: Donald foaf_name: Richards, Donald foaf_surname: Richards dct_date: 2014^xs_gYear dct_publisher: ArXiv@ dct_title: ' Exact formulas for the normalizing constants of Wishart distributions for graphical models@' ...