Modeling Discrete Interventional Data using Directed Cyclic Graphical Models

Mark Schmidt, Kevin Murphy
Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, PMLR R7:495-503, 2009.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR7-schmidt09a, title = {Modeling Discrete Interventional Data using Directed Cyclic Graphical Models}, author = {Schmidt, Mark and Murphy, Kevin}, booktitle = {Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence}, pages = {495--503}, year = {2009}, editor = {Bilmes, Jeff and Ng, Andrew Y.}, volume = {R7}, series = {Proceedings of Machine Learning Research}, month = {18--21 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r7/main/assets/schmidt09a/schmidt09a.pdf}, url = {https://proceedings.mlr.press/r7/schmidt09a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Modeling Discrete Interventional Data using Directed Cyclic Graphical Models %A Mark Schmidt %A Kevin Murphy %B Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2009 %E Jeff Bilmes %E Andrew Y. Ng %F pmlr-vR7-schmidt09a %I PMLR %P 495--503 %U https://proceedings.mlr.press/r7/schmidt09a.html %V R7 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Schmidt, M. & Murphy, K.. (2009). Modeling Discrete Interventional Data using Directed Cyclic Graphical Models. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R7:495-503 Available from https://proceedings.mlr.press/r7/schmidt09a.html. Reissued by PMLR on 04 October 2026.

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