Gaussian graphical model determination based on birth-death MCMC inference
Reza Mohammadi, Ernst C. Wit · 2013
We propose an ecient Bayesian methodology for model determination in Gaussian graphical models for both decomposable and nondecomposable cases. The proposed methodology is a trans-dimensional MCMC approach, which makes use of the spatial birth-death process. The birth-death process jumps through all possible graphical models by adding a new edge in a birth event or deleting an edge in a death event. The proposed method is easy to implement and computationally feasible for large graphical models. We illustrate the eciency of the proposed methodology on simulated and real datasets. Besides, we have implemented the proposed methodology into an R-package, called BDgraph which is freely available online.