A MCMC Approach for Learning the Structure of Gaussian Acyclic Directed Mixed Graphs
نویسنده
چکیده
Graphical models are widely used to encode conditional independence constraints and causal assumptions, the directed acyclic graph (DAG) being one of the most common types of models. However, DAGs are not closed under marginalization: that is, a chosen marginal of a distribution Markov to a DAG might not be representable with another DAG, unless one discards some of the structural independencies. Acyclic directed mixed graphs (ADMGs) generalize DAGs so that closure under marginalization is possible. In a previous work, we showed how to perform Bayesian inference to infer the posterior distribution of the parameters of a given Gaussian ADMG model, where the graph is fixed. In this paper, we extend this procedure to allow for priors over graph structures.
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