ML Anthology

The Dynamic Hierarchical Dirichlet Process

Lu Ren, David B. Dunson, Lawrence Carin
ICML 2008 pp. 824-831
doi:10.1145/1390156.1390260 /icml/2008/ren2008icml-dynamic/

Abstract

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 representation 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.

PDF ICML Semantic Scholar

Cite

Text

Ren et al. "The Dynamic Hierarchical Dirichlet Process." International Conference on Machine Learning, 2008. doi:10.1145/1390156.1390260

Markdown

[Ren et al. "The Dynamic Hierarchical Dirichlet Process." International Conference on Machine Learning, 2008.](https://mlanthology.org/icml/2008/ren2008icml-dynamic/) doi:10.1145/1390156.1390260

BibTeX

@inproceedings{ren2008icml-dynamic,
  title     = {{The Dynamic Hierarchical Dirichlet Process}},
  author    = {Ren, Lu and Dunson, David B. and Carin, Lawrence},
  booktitle = {International Conference on Machine Learning},
  year      = {2008},
  pages     = {824-831},
  doi       = {10.1145/1390156.1390260},
  url       = {https://mlanthology.org/icml/2008/ren2008icml-dynamic/}
}