On a wider class of prior distributions for graphical models

Abstract

Gaussian graphical models are useful tools for conditional independence structure inference of multivariate random variables. Unfortunately, Bayesian inference of latent graph structures is challenging due to exponential growth of ${\mathcal{G}}_n$, the set of all graphs in $n$ vertices. One approach that has been proposed to tackle this problem is to limit search to subsets of ${\mathcal{G}}_n$. In this paper, we study subsets that are vector subspaces with the cycle space ${\mathcal{C}}_n$ as main example. We propose a novel prior on ${\mathcal{C}}_n$ based on linear combinations of cycle basis elements and present its theoretical properties. Using this prior, we implement a Markov chain Monte Carlo algorithm, and show that (i) posterior edge inclusion estimates computed with our technique are comparable to estimates from the standard technique despite searching a smaller graph space, and (ii) the vector space perspective enables straightforward implementation of MCMC algorithms.

37 pages, 8 figures

References in corpus (4)

• MCMC for doubly-intractable distributions
• Copula Gaussian graphical models and their application to modeling functional disability data
• Unbiased approximation of posteriors via coupled particle Markov chain Monte Carlo
• Bayesian Spanning Tree: Estimating the Backbone of the Dependence Graph