Bayesian Inference for Gaussian Mixed Graph Models

Ricardo Silva


  We introduce priors and algorithms to perform Bayesian inference in Gaussian models defined by acyclic directed mixed graphs. Such a class of graphs, composed of directed and bi-directed edges, is a representation of conditional independencies that is closed under marginalization and arises naturally from causal models which allow for unmeasured confounding. Monte Carlo methods and a variational approximation for such models are presented. Our algorithms for Bayesian inference allow the evaluation of posterior distributions for several quantities of interest, including causal effects that are not identifiable from data alone but could otherwise be inferred where informative prior knowledge about confounding is available.
Joint work with Zoubin Ghahramani

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Pradeep Ravikumar
Last modified: Thu Apr 13 11:46:00 EDT 2006