Mixed Cumulative Distribution Networks

Ricardo Silva, Charles Blundell, Yee Whye Teh
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:670-678, 2011.

Abstract

Directed acyclic graphs (DAGs) are a popular framework to express multivariate probability distributions. Acyclic directed mixed graphs (ADMGs) are generalizations of DAGs that can succinctly capture much richer sets of conditional independencies, and are especially useful in modeling the effects of latent variables implicitly. Unfortunately, there are currently no parameterizations of general ADMGs. In this paper, we apply recent work on cumulative distribution networks and copulas to propose one general construction for ADMG models. We consider a simple parameter estimation approach, and report some encouraging experimental results.

Cite this Paper

BibTeX
@InProceedings{pmlr-v15-silva11a, title = {Mixed Cumulative Distribution Networks}, author = {Silva, Ricardo and Blundell, Charles and Teh, Yee Whye}, booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics}, pages = {670--678}, year = {2011}, editor = {Gordon, Geoffrey and Dunson, David and Dudík, Miroslav}, volume = {15}, series = {Proceedings of Machine Learning Research}, address = {Fort Lauderdale, FL, USA}, month = {11--13 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v15/silva11a/silva11a.pdf}, url = {https://proceedings.mlr.press/v15/silva11a.html}, abstract = {Directed acyclic graphs (DAGs) are a popular framework to express multivariate probability distributions. Acyclic directed mixed graphs (ADMGs) are generalizations of DAGs that can succinctly capture much richer sets of conditional independencies, and are especially useful in modeling the effects of latent variables implicitly. Unfortunately, there are currently no parameterizations of general ADMGs. In this paper, we apply recent work on cumulative distribution networks and copulas to propose one general construction for ADMG models. We consider a simple parameter estimation approach, and report some encouraging experimental results.} }
Endnote
%0 Conference Paper %T Mixed Cumulative Distribution Networks %A Ricardo Silva %A Charles Blundell %A Yee Whye Teh %B Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2011 %E Geoffrey Gordon %E David Dunson %E Miroslav Dudík %F pmlr-v15-silva11a %I PMLR %P 670--678 %U https://proceedings.mlr.press/v15/silva11a.html %V 15 %X Directed acyclic graphs (DAGs) are a popular framework to express multivariate probability distributions. Acyclic directed mixed graphs (ADMGs) are generalizations of DAGs that can succinctly capture much richer sets of conditional independencies, and are especially useful in modeling the effects of latent variables implicitly. Unfortunately, there are currently no parameterizations of general ADMGs. In this paper, we apply recent work on cumulative distribution networks and copulas to propose one general construction for ADMG models. We consider a simple parameter estimation approach, and report some encouraging experimental results.
RIS
TY - CPAPER TI - Mixed Cumulative Distribution Networks AU - Ricardo Silva AU - Charles Blundell AU - Yee Whye Teh BT - Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics DA - 2011/06/14 ED - Geoffrey Gordon ED - David Dunson ED - Miroslav Dudík ID - pmlr-v15-silva11a PB - PMLR DP - Proceedings of Machine Learning Research VL - 15 SP - 670 EP - 678 L1 - http://proceedings.mlr.press/v15/silva11a/silva11a.pdf UR - https://proceedings.mlr.press/v15/silva11a.html AB - Directed acyclic graphs (DAGs) are a popular framework to express multivariate probability distributions. Acyclic directed mixed graphs (ADMGs) are generalizations of DAGs that can succinctly capture much richer sets of conditional independencies, and are especially useful in modeling the effects of latent variables implicitly. Unfortunately, there are currently no parameterizations of general ADMGs. In this paper, we apply recent work on cumulative distribution networks and copulas to propose one general construction for ADMG models. We consider a simple parameter estimation approach, and report some encouraging experimental results. ER -
APA
Silva, R., Blundell, C. & Teh, Y.W.. (2011). Mixed Cumulative Distribution Networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 15:670-678 Available from https://proceedings.mlr.press/v15/silva11a.html.

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