Bayesian model averaging and model selection for markov equivalence classes of acyclic digraphs

Abstract

Acyclic digraphs (ADGs) are widely used to describe dependences among variables in multivariate distributions. In particular, the likelihood functions of ADG models admit convenient recursive factorizations that often allow explicit maximum likelihood estimates and that are well suited to building Bayesian networks for expert systems. There may, however, be many ADGs that determine the same dependence (= Markov) model. Thus, the family of all ADGs with a given set of vertices is naturally partitioned into Markov-equivalence classes, each class being associated with a unique statistical model. Statistical procedures, such as model selection or model averaging, that fail to take into account these equivalence classes, may incur substantial computational or other inefficiencies. Recent results have shown that each Markov-equivalence class is uniquely determined by a single chain graph, the essential graph, that is itself Markov-equivalent simultaneously to all ADGs in the equivalence class. Here we propose two stochastic Bayesian model averaging and selection algorithms for essential graphs and apply them to the analysis of three discrete-variable data sets.

Keywords:

• Bayesian graphical model
• Essential graph
• model uncertainty
• model averaging
• Markov equivalence
• Markov chain Monte Carlo