The dynamic hierarchical Dirichlet process

Lu Ren, David B. Dunson, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

91 Scopus citations

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 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 languageEnglish (US)
Title of host publicationProceedings of the 25th International Conference on Machine Learning
PublisherAssociation for Computing Machinery (ACM)
Pages824-831
Number of pages8
ISBN (Print)9781605582054
DOIs
StatePublished - Jan 1 2008
Externally publishedYes

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