The dynamic hierarchical Dirichlet process

Published

Journal Article

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).

Full Text

Duke Authors

Cited Authors

  • Ren, L; Dunson, DB; Carin, L

Published Date

  • January 1, 2008

Published In

  • Proceedings of the 25th International Conference on Machine Learning

Start / End Page

  • 824 - 831

Digital Object Identifier (DOI)

  • 10.1145/1390156.1390260

Citation Source

  • Scopus