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The dynamic hierarchical Dirichlet process

Publication ,  Journal Article
Ren, L; Dunson, DB; Carin, L
Published in: Proceedings of the 25th International Conference on Machine Learning
January 1, 2008

The dynamic hierarchical Dirichlet process (dHDP) is developed to model the time-evolving statistical properties of sequential data sets. The data collected at any time point are represented via a mixture associated with an appropriate underlying model, in the framework of HDP. The statistical properties of data collected at consecutive time points are linked via a random parameter that controls their probabilistic similarity. The sharing mechanisms of the time-evolving data are derived, and a relatively simple Markov Chain Monte Carlo sampler is developed. Experimental results are presented to demonstrate the model. Copyright 2008 by the author(s)/owner(s).

Duke Scholars

Published In

Proceedings of the 25th International Conference on Machine Learning

DOI

Publication Date

January 1, 2008

Start / End Page

824 / 831
 

Citation

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Ren, L., Dunson, D. B., & Carin, L. (2008). The dynamic hierarchical Dirichlet process. Proceedings of the 25th International Conference on Machine Learning, 824–831. https://doi.org/10.1145/1390156.1390260
Ren, L., D. B. Dunson, and L. Carin. “The dynamic hierarchical Dirichlet process.” Proceedings of the 25th International Conference on Machine Learning, January 1, 2008, 824–31. https://doi.org/10.1145/1390156.1390260.
Ren L, Dunson DB, Carin L. The dynamic hierarchical Dirichlet process. Proceedings of the 25th International Conference on Machine Learning. 2008 Jan 1;824–31.
Ren, L., et al. “The dynamic hierarchical Dirichlet process.” Proceedings of the 25th International Conference on Machine Learning, Jan. 2008, pp. 824–31. Scopus, doi:10.1145/1390156.1390260.
Ren L, Dunson DB, Carin L. The dynamic hierarchical Dirichlet process. Proceedings of the 25th International Conference on Machine Learning. 2008 Jan 1;824–831.

Published In

Proceedings of the 25th International Conference on Machine Learning

DOI

Publication Date

January 1, 2008

Start / End Page

824 / 831