×

Bayesian inference for nondecomposable graphical Gaussian models. (English) Zbl 1192.62090

Summary: We propose a method to calculate the posterior probability of a nondecomposable graphical Gaussian model. Our proposal is based on a new device to sample from Wishart distributions, conditional on the graphical constraints. As a result, our methodology allows Bayesian model selection within the whole class of graphical Gaussian models, including nondecomposable ones.

MSC:

62F15 Bayesian inference
05C90 Applications of graph theory
62D05 Sampling theory, sample surveys
PDFBibTeX XMLCite