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 language||American English|
|Number of pages||9|
|Journal||Proceedings of Machine Learning Research|
|State||Published - 2013|
|Event||16th International Conference on Artificial Intelligence and Statistics, AISTATS 2013 - Scottsdale, United States|
Duration: 29 Apr 2013 → 1 May 2013
Bibliographical notePublisher Copyright:
Copyright 2013 by the authors.