Bayesian Model Averaging and Model Selection for Markov Equivalence Classes of Acyclic Digraphs

  title={Bayesian Model Averaging and Model Selection for Markov Equivalence Classes of Acyclic Digraphs},
  author={David Madigan and Steen A. Andersson and Michael D. Perlman and Chris Volinsky},
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… CONTINUE READING
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