Uncertainty in Artificial Intelligence
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Parallel Markov Chain Monte Carlo for Pitman-Yor Mixture Models
Kumar Dubey, Sinead Williamson, Eric Xing
Abstract:
The Pitman-Yor process provides an elegant way to cluster data that exhibit power law behavior, where the number of clusters is unknown or un- bounded. Unfortunately, inference in Pitman- Yor process-based models is typically slow and does not scale well with dataset size. In this paper we present new auxiliary-variable repre- sentations for the Pitman-Yor process and a spe- cial case of the hierarchical Pitman-Yor process that allows us to develop parallel inference algo- rithms that distribute inference both on the data space and the model space. We show that our method scales well with increasing data while avoiding any degradation in estimate quality.
Keywords:
Pages: 142-151
PS Link:
PDF Link: /papers/14/p142-dubey.pdf
BibTex:
@INPROCEEDINGS{Dubey14,
AUTHOR = "Kumar Dubey and Sinead Williamson and Eric Xing",
TITLE = "Parallel Markov Chain Monte Carlo for Pitman-Yor Mixture Models",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "142--151"
}


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