The dynamic hierarchical Dirichlet process

The dynamic hierarchical Dirichlet process (dHDP) is developed to model the timeevolving 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 timeevolving data are derived, and a relatively simple Markov Chain Monte Carlo sampler is developed. Experimental results are presented to demonstrate the model.

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Lu Ren, David B. Dunson, Lawrence Carin
• Keywords: Appropriate Underlying Model, Dynamic Hierarchical Dirichlet, ICML 2008, Machine Learning, Sequential Data Sets