Speedy Model Selection (SMS) for Copula Models

Yaniv Tenzer, Gal Elidan
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:668-677, 2013.

Abstract

We tackle the challenge of efficiently learning the structure of expressive multivariate realvalued densities of copula graphical models. We start by theoretically substantiating the conjecture that for many copula families the magnitude of Spearman’s rank correlation coefficient is monotonic in the expected contribution of an edge in network, namely the negative copula entropy. We then build on this theory and suggest a novel Bayesian approach that makes use of a prior over values of Spearman’s rho for learning copula-based models that involve a mix of copula families. We demonstrate the generalization effectiveness of our highly efficient approach on sizable and varied real-life datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR11-tenzer13a, title = {Speedy Model Selection ({SMS}) for Copula Models}, author = {Tenzer, Yaniv and Elidan, Gal}, booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence}, pages = {668--677}, year = {2013}, editor = {Nicholson, Ann and Smyth, Padhraic}, volume = {R11}, series = {Proceedings of Machine Learning Research}, month = {12--14 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r11/main/assets/tenzer13a/tenzer13a.pdf}, url = {https://proceedings.mlr.press/r11/tenzer13a.html}, abstract = {We tackle the challenge of efficiently learning the structure of expressive multivariate realvalued densities of copula graphical models. We start by theoretically substantiating the conjecture that for many copula families the magnitude of Spearman’s rank correlation coefficient is monotonic in the expected contribution of an edge in network, namely the negative copula entropy. We then build on this theory and suggest a novel Bayesian approach that makes use of a prior over values of Spearman’s rho for learning copula-based models that involve a mix of copula families. We demonstrate the generalization effectiveness of our highly efficient approach on sizable and varied real-life datasets.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Speedy Model Selection (SMS) for Copula Models %A Yaniv Tenzer %A Gal Elidan %B Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2013 %E Ann Nicholson %E Padhraic Smyth %F pmlr-vR11-tenzer13a %I PMLR %P 668--677 %U https://proceedings.mlr.press/r11/tenzer13a.html %V R11 %X We tackle the challenge of efficiently learning the structure of expressive multivariate realvalued densities of copula graphical models. We start by theoretically substantiating the conjecture that for many copula families the magnitude of Spearman’s rank correlation coefficient is monotonic in the expected contribution of an edge in network, namely the negative copula entropy. We then build on this theory and suggest a novel Bayesian approach that makes use of a prior over values of Spearman’s rho for learning copula-based models that involve a mix of copula families. We demonstrate the generalization effectiveness of our highly efficient approach on sizable and varied real-life datasets. %Z Reissued by PMLR on 04 October 2026.
APA
Tenzer, Y. & Elidan, G.. (2013). Speedy Model Selection (SMS) for Copula Models. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R11:668-677 Available from https://proceedings.mlr.press/r11/tenzer13a.html. Reissued by PMLR on 04 October 2026.

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