Variational Bayesian Dirichlet-Multinomial Allocation for Exponential Family Mixtures

  • Shipeng Yu
  • Kai Yu
  • Volker Tresp
  • Hans-Peter Kriegel
Conference paper

DOI: 10.1007/11871842_87

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4212)
Cite this paper as:
Yu S., Yu K., Tresp V., Kriegel HP. (2006) Variational Bayesian Dirichlet-Multinomial Allocation for Exponential Family Mixtures. In: Fürnkranz J., Scheffer T., Spiliopoulou M. (eds) Machine Learning: ECML 2006. ECML 2006. Lecture Notes in Computer Science, vol 4212. Springer, Berlin, Heidelberg

Abstract

This paper studies a Bayesian framework for density modeling with mixture of exponential family distributions. Variational Bayesian Dirichlet-Multinomial allocation (VBDMA) is introduced, which performs inference and learning efficiently using variational Bayesian methods and performs automatic model selection. The model is closely related to Dirichlet process mixture models and demonstrates similar automatic model selection in the variational Bayesian context.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shipeng Yu
    • 1
    • 2
  • Kai Yu
    • 2
  • Volker Tresp
    • 2
  • Hans-Peter Kriegel
    • 1
  1. 1.Institute for Computer ScienceUniversity of MunichGermany
  2. 2.Siemens Corporate TechnologyMunichGermany

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