The dynamic hierarchical Dirichlet process
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