A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models
非可分解高斯图模型边际似然的蒙特卡洛计算
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…