Variational Bayesian Dirichlet-Multinomial Allocation for Exponential Family Mixtures
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- 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
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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