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. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Yu, S., Yu, K., Tresp, V., & Kriegel, H. P. (2006). Variational Bayesian dirichlet-multinomial allocation for exponential family mixtures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4212 LNAI, pp. 841–848). Springer Verlag. https://doi.org/10.1007/11871842_87
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