Parallel Markov Chain Monte Carlo for Pitman-Yor Mixture Models

Kumar Avinava Dubey, Sinead Williamson, Eric P. Xing
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:696-705, 2014.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-dubey14a, title = {Parallel {M}arkov Chain {M}onte {C}arlo for Pitman-Yor Mixture Models}, author = {Dubey, Kumar Avinava and Williamson, Sinead and Xing, Eric P.}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {696--705}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/dubey14a/dubey14a.pdf}, url = {https://proceedings.mlr.press/r12/dubey14a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Parallel Markov Chain Monte Carlo for Pitman-Yor Mixture Models %A Kumar Avinava Dubey %A Sinead Williamson %A Eric P. Xing %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-dubey14a %I PMLR %P 696--705 %U https://proceedings.mlr.press/r12/dubey14a.html %V R12 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Dubey, K.A., Williamson, S. & Xing, E.P.. (2014). Parallel Markov Chain Monte Carlo for Pitman-Yor Mixture Models. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:696-705 Available from https://proceedings.mlr.press/r12/dubey14a.html. Reissued by PMLR on 04 October 2026.

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