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Journal Club: The Dynamic Hierarchical Dirichlet Process

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Journal Club on the ICML08 paper “The Dynamic Hierarchical Dirichlet Process”. (http://www.ece.duke.edu/~lcarin/dHDP_ICMLv7.pdf)

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 associ- ated 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 parame- ter that controls their probabilistic similar- ity. The sharing mechanisms of the time- evolving data are derived, and a relatively simple Markov Chain Monte Carlo sampler is developed. Experimental results are pre- sented to demonstrate the model.

This talk is part of the Machine Learning Journal Club series.

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