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).
|Original language||English (US)|
|Title of host publication||Proceedings of the 25th International Conference on Machine Learning|
|Publisher||Association for Computing Machinery (ACM)|
|Number of pages||8|
|State||Published - Jan 1 2008|