Séance Séminaire

Séminaire de Probabilités et Statistique

Network inference via birth-death MCMC

We propose a Bayesian strategy for model determination in Gaussian graphical models for both decomposable and non-decomposable cases. The proposed methodology is a trans-dimensional MCMC approach, which makes use of a birth-death process. In particular, the birth-death process updates the graph by adding a new edge in a birth event or deleting an edge in a death event. It is easy to implement, computationally feasible for large graphical models. Unlike frequentist approaches, our method gives a principled and, in practice, sensible approach for model selection, as we show in a cell signaling example. We illustrate the efficiency of the proposed methodology on simulated and real datasets. Moreover, we implemented the proposed methodology into an R-package, called BDgraph.