Uncertainty in Artificial Intelligence
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Modeling Discrete Interventional Data using Directed Cyclic Graphical Models
Mark Schmidt, Kevin Murphy
Abstract:
We outline a representation for discrete multivariate distributions in terms of interventional potential functions that are globally normalized. This representation can be used to model the effects of interventions, and the independence properties encoded in this model can be represented as a directed graph that allows cycles. In addition to discussing inference and sampling with this representation, we give an exponential family parametrization that allows parameter estimation to be stated as a convex optimization problem; we also give a convex relaxation of the task of simultaneous parameter and structure learning using group l1-regularization. The model is evaluated on simulated data and intracellular flow cytometry data.
Keywords: null
Pages: 487-495
PS Link:
PDF Link: /papers/09/p487-schmidt.pdf
BibTex:
@INPROCEEDINGS{Schmidt09,
AUTHOR = "Mark Schmidt and Kevin Murphy",
TITLE = "Modeling Discrete Interventional Data using Directed Cyclic Graphical Models",
BOOKTITLE = "Proceedings of the Twenty-Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-09)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2009",
PAGES = "487--495"
}


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