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 mix...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.Read More
Publication Year: 2008
Publication Date: 2008-01-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 116
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