Scale-free network priors in Bayesian inference with applications to bioinformatics

Abstract

This paper integrates scale-free network properties into statistical inference. This is accomplished in a meaningful manner by devising scale-free prior distributions based on three well-known scale-free network models in the framework of Gaussian graphical models. The new priors are compared with a random network prior via an extensive Markov chain Monte Carlo simulation. As well, a numerical example using microarray data to infer a protein-protein interaction network is provided.