Learning Markov Equivalence Classes of Directed Acyclic Graphs: an Objective Bayes Approach

Federico Castelletti, Guido Consonni, Marco Luigi Della Vedova, Stefano Peluso*

*Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in rivistaArticolo in rivista

8 Citazioni (Scopus)


A Markov equivalence class contains all the Directed Acyclic Graphs (DAGs) encoding the same conditional independencies, and is represented by a Completed Partially Directed Acyclic Graph (CPDAG), also named Essential Graph (EG).We approach the problem of model selection among noncausal sparse Gaussian DAGs by directly scoring EGs, using an objective Bayes method. Specifically, we construct objective priors for model selection based on the Fractional Bayes Factor, leading to a closed form expression for the marginal likelihood of an EG. Next we propose an MCMC strategy to explore the space of EGs using sparsity constraints, and illustrate the performance of our method on simulation studies, as well as on a real dataset. Our method provides a coherent quantication of inferential uncertainty, requires minimal prior specication, and shows to be competitive in learning the structure of the data-generating EG when compared to alternative state-of-the-art algorithms.
Lingua originaleEnglish
pagine (da-a)1231-1256
Numero di pagine26
RivistaBayesian Analysis
Stato di pubblicazionePubblicato - 2018


  • Bayesian model selection
  • Essential graph
  • Fractional Bayes factor
  • Graphical model


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