Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models

Atay-Kayis A., MASSAM H.

BIOMETRIKA, vol.92, no.2, pp.317-335, 2005 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 92 Issue: 2
  • Publication Date: 2005
  • Doi Number: 10.1093/biomet/92.2.317
  • Journal Name: BIOMETRIKA
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.317-335
  • Süleyman Demirel University Affiliated: Yes


A centred Gaussian model that is Markov with respect to an undirected graph G is characterised by the parameter set of its precision matrices which is the cone M+(G) of positive definite matrices with entries corresponding to the missing edges of G constrained to be equal to zero. In a Bayesian framework, the conjugate family for the precision parameter is the distribution with Wishart density with respect to the Lebesgue measure restricted to M+(G). We call this distribution the G-Wishart. When G is nondecomposable, the normalising constant of the G-Wishart cannot be computed in closed form. In this paper, we give a simple Monte Carlo method for computing this normalising constant. The main feature of our method is that the sampling distribution is exact and consists of a product of independent univariate standard normal and chi-squared distributions that can be read off the graph G. Computing this normalising constant is necessary for obtaining the posterior distribution of G or the marginal likelihood of the corresponding graphical Gaussian model. Our method also gives a way of sampling from the posterior distribution of the precision matrix.