Dynamic copula networks for modeling real-valued time series

Elad Eban, Gideon Rothschild, Adi Mizrahi, Israel Nelken, Gal Elidan

Research output: Contribution to journalConference articlepeer-review

10 Scopus citations


Probabilistic modeling of temporal phenom- ena is of central importance in a variety of fields ranging from neuroscience to economics to speech recognition. While the task has received extensive attention in recent decades, learning temporal models for multivariate real-valued data that is non-Gaussian is still a formidable challenge. Recently, the power of copulas, a framework for representing complex multi-modal and heavy-tailed distributions, was fused with the formalism of Bayesian networks to allow for flexible modeling of high- dimensional distributions. In this work we introduce Dynamic Copula Bayesian Networks (DCBNs), a generalization aimed at capturing the distribution of rich temporal sequences. We apply our model to three markedly different real-life domains and demonstrate substantial quantitative and qualitative advantages.

Original languageAmerican English
Pages (from-to)247-255
Number of pages9
JournalProceedings of Machine Learning Research
StatePublished - 2013
Event16th International Conference on Artificial Intelligence and Statistics, AISTATS 2013 - Scottsdale, United States
Duration: 29 Apr 20131 May 2013

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Copyright 2013 by the authors.

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