ML Anthology

Variational Bayesian Dirichlet-Multinomial Allocation for Exponential Family Mixtures

Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Kriegel
ECML-PKDD 2006 pp. 841-848
doi:10.1007/11871842_87 /ecmlpkdd/2006/yu2006ecml-variational/

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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Cite

Text

Yu et al. "Variational Bayesian Dirichlet-Multinomial Allocation for Exponential Family Mixtures." European Conference on Machine Learning, 2006. doi:10.1007/11871842_87

Markdown

[Yu et al. "Variational Bayesian Dirichlet-Multinomial Allocation for Exponential Family Mixtures." European Conference on Machine Learning, 2006.](https://mlanthology.org/ecmlpkdd/2006/yu2006ecml-variational/) doi:10.1007/11871842_87

BibTeX

@inproceedings{yu2006ecml-variational,
  title     = {{Variational Bayesian Dirichlet-Multinomial Allocation for Exponential Family Mixtures}},
  author    = {Yu, Shipeng and Yu, Kai and Tresp, Volker and Kriegel, Hans-Peter},
  booktitle = {European Conference on Machine Learning},
  year      = {2006},
  pages     = {841-848},
  doi       = {10.1007/11871842_87},
  url       = {https://mlanthology.org/ecmlpkdd/2006/yu2006ecml-variational/}
}