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Augment-and-conquer negative binomial processes

Publication ,  Journal Article
Zhou, M; Carin, L
Published in: Advances in Neural Information Processing Systems
December 1, 2012

By developing data augmentation methods unique to the negative binomial (NB) distribution, we unite seemingly disjoint count and mixture models under the NB process framework. We develop fundamental properties of the models and derive efficient Gibbs sampling inference. We show that the gamma-NB process can be reduced to the hierarchical Dirichlet process with normalization, highlighting its unique theoretical, structural and computational advantages. A variety of NB processes with distinct sharing mechanisms are constructed and applied to topic modeling, with connections to existing algorithms, showing the importance of inferring both the NB dispersion and probability parameters.

Duke Scholars

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

Publication Date

December 1, 2012

Volume

4

Start / End Page

2546 / 2554

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology
 

Citation

APA
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ICMJE
MLA
NLM
Zhou, M., & Carin, L. (2012). Augment-and-conquer negative binomial processes. Advances in Neural Information Processing Systems, 4, 2546–2554.
Zhou, M., and L. Carin. “Augment-and-conquer negative binomial processes.” Advances in Neural Information Processing Systems 4 (December 1, 2012): 2546–54.
Zhou M, Carin L. Augment-and-conquer negative binomial processes. Advances in Neural Information Processing Systems. 2012 Dec 1;4:2546–54.
Zhou, M., and L. Carin. “Augment-and-conquer negative binomial processes.” Advances in Neural Information Processing Systems, vol. 4, Dec. 2012, pp. 2546–54.
Zhou M, Carin L. Augment-and-conquer negative binomial processes. Advances in Neural Information Processing Systems. 2012 Dec 1;4:2546–2554.

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

Publication Date

December 1, 2012

Volume

4

Start / End Page

2546 / 2554

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology