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Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization
[article]
2021
arXiv
pre-print
The finite invert Beta-Liouville mixture model (IBLMM) has recently gained some attention due to its positive data modeling capability. Under the conventional variational inference (VI) framework, the analytically tractable solution to the optimization of the variational posterior distribution cannot be obtained, since the variational object function involves evaluation of intractable moments. With the recently proposed extended variational inference (EVI) framework, a new function is proposed
arXiv:2112.14375v1
fatcat:25r5oiggufgdhl2sgsgdvrpux4