Learning mixed graphical models from data with p larger than n

Inma Tur, Robert Castelo
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:762-770, 2011.

Abstract

Structure learning of Gaussian graphical models is an extensively studied problem in the classical multivariate setting where the sample size n is larger than the number of random variables p, as well as in the more challenging setting when p>>n. However, analogous approaches for learning the structure of graphical models with mixed discrete and continuous variables when p>>n remain largely unexplored. Here we describe a statistical learning procedure for this problem based on limited-order correlations and assess its performance with synthetic and real data.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR9-tur11a, title = {Learning mixed graphical models from data with p larger than n}, author = {Tur, Inma and Castelo, Robert}, booktitle = {Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence}, pages = {762--770}, year = {2011}, editor = {Cozman, Fabio and Pfeffer, Avi}, volume = {R9}, series = {Proceedings of Machine Learning Research}, month = {14--17 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r9/main/assets/tur11a/tur11a.pdf}, url = {https://proceedings.mlr.press/r9/tur11a.html}, abstract = {Structure learning of Gaussian graphical models is an extensively studied problem in the classical multivariate setting where the sample size n is larger than the number of random variables p, as well as in the more challenging setting when p>>n. However, analogous approaches for learning the structure of graphical models with mixed discrete and continuous variables when p>>n remain largely unexplored. Here we describe a statistical learning procedure for this problem based on limited-order correlations and assess its performance with synthetic and real data.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Learning mixed graphical models from data with p larger than n %A Inma Tur %A Robert Castelo %B Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2011 %E Fabio Cozman %E Avi Pfeffer %F pmlr-vR9-tur11a %I PMLR %P 762--770 %U https://proceedings.mlr.press/r9/tur11a.html %V R9 %X Structure learning of Gaussian graphical models is an extensively studied problem in the classical multivariate setting where the sample size n is larger than the number of random variables p, as well as in the more challenging setting when p>>n. However, analogous approaches for learning the structure of graphical models with mixed discrete and continuous variables when p>>n remain largely unexplored. Here we describe a statistical learning procedure for this problem based on limited-order correlations and assess its performance with synthetic and real data. %Z Reissued by PMLR on 04 October 2026.
APA
Tur, I. & Castelo, R.. (2011). Learning mixed graphical models from data with p larger than n. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R9:762-770 Available from https://proceedings.mlr.press/r9/tur11a.html. Reissued by PMLR on 04 October 2026.

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