Uncertainty in Artificial Intelligence
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Bayesian Inference for Gaussian Mixed Graph Models
Ricardo Silva, Zoubin Ghahramani
Abstract:
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.
Keywords:
Pages: 453-460
PS Link:
PDF Link: /papers/06/p453-silva.pdf
BibTex:
@INPROCEEDINGS{Silva06,
AUTHOR = "Ricardo Silva and Zoubin Ghahramani",
TITLE = "Bayesian Inference for Gaussian Mixed Graph Models",
BOOKTITLE = "Proceedings of the Twenty-Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-06)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2006",
PAGES = "453--460"
}


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