Maximum likelihood fitting of acyclic directed mixed graphs to binary data

Robin Evans, Thomas Richardson
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:193-200, 2010.

Abstract

Acyclic directed mixed graphs, also known as semi-Markov models represent the condi- tional independence structure induced on an observed margin by a DAG model with la- tent variables. In this paper we present the first method for fitting these models to binary data using maximum likelihood estimation.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-evans10a, title = {Maximum likelihood fitting of acyclic directed mixed graphs to binary data}, author = {Evans, Robin and Richardson, Thomas}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {193--200}, year = {2010}, editor = {Grünwald, Peter and Spirtes, Peter}, volume = {R8}, series = {Proceedings of Machine Learning Research}, month = {08--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r8/main/assets/evans10a/evans10a.pdf}, url = {https://proceedings.mlr.press/r8/evans10a.html}, abstract = {Acyclic directed mixed graphs, also known as semi-Markov models represent the condi- tional independence structure induced on an observed margin by a DAG model with la- tent variables. In this paper we present the first method for fitting these models to binary data using maximum likelihood estimation.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Maximum likelihood fitting of acyclic directed mixed graphs to binary data %A Robin Evans %A Thomas Richardson %B Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2010 %E Peter Grünwald %E Peter Spirtes %F pmlr-vR8-evans10a %I PMLR %P 193--200 %U https://proceedings.mlr.press/r8/evans10a.html %V R8 %X Acyclic directed mixed graphs, also known as semi-Markov models represent the condi- tional independence structure induced on an observed margin by a DAG model with la- tent variables. In this paper we present the first method for fitting these models to binary data using maximum likelihood estimation. %Z Reissued by PMLR on 04 October 2026.
APA
Evans, R. & Richardson, T.. (2010). Maximum likelihood fitting of acyclic directed mixed graphs to binary data. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:193-200 Available from https://proceedings.mlr.press/r8/evans10a.html. Reissued by PMLR on 04 October 2026.

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