A factorization criterion for acyclic directed mixed graphs

Thomas Richardson
Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, PMLR R7:470-478, 2009.

Abstract

Acyclic directed mixed graphs, also known as semi-Markov models represent the conditional independence structure induced on an observed margin by a DAG model with latent variables. In this paper we present a factorization criterion for these models that is equivalent to the global Markov property given by (the natural extension of) d-separation.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR7-richardson09a, title = {A factorization criterion for acyclic directed mixed graphs}, author = {Richardson, Thomas}, booktitle = {Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence}, pages = {470--478}, 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/richardson09a/richardson09a.pdf}, url = {https://proceedings.mlr.press/r7/richardson09a.html}, abstract = {Acyclic directed mixed graphs, also known as semi-Markov models represent the conditional independence structure induced on an observed margin by a DAG model with latent variables. In this paper we present a factorization criterion for these models that is equivalent to the global Markov property given by (the natural extension of) d-separation.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T A factorization criterion for acyclic directed mixed graphs %A Thomas Richardson %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-richardson09a %I PMLR %P 470--478 %U https://proceedings.mlr.press/r7/richardson09a.html %V R7 %X Acyclic directed mixed graphs, also known as semi-Markov models represent the conditional independence structure induced on an observed margin by a DAG model with latent variables. In this paper we present a factorization criterion for these models that is equivalent to the global Markov property given by (the natural extension of) d-separation. %Z Reissued by PMLR on 04 October 2026.
APA
Richardson, T.. (2009). A factorization criterion for acyclic directed mixed graphs. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R7:470-478 Available from https://proceedings.mlr.press/r7/richardson09a.html. Reissued by PMLR on 04 October 2026.

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