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• W2558549895 abstract "A general modeling framework is proposed that unifies nonparametric-Bayesian models, topic-models and Bayesian networks. This class of infinite state Bayes nets (ISBN) can be viewed as directed networks of 'hierarchical Dirichlet processes' (HDPs) where the domain of the variables can be structured (e.g. words in documents or features in images). We show that collapsed Gibbs sampling can be done efficiently in these models by leveraging the structure of the Bayes net and using the forward-filtering-backward-sampling algorithm for junction trees. Existing models, such as nested-DP, Pachinko allocation, mixed membership stochastic block models as well as a number of new models are described as ISBNs. Two experiments have been performed to illustrate these ideas." @default.
• W2558549895 created "2016-12-08" @default.
• W2558549895 creator A5077309348 @default.
• W2558549895 creator A5087368991 @default.
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• W2558549895 date "2007-12-03" @default.
• W2558549895 modified "2023-09-23" @default.
• W2558549895 title "Infinite state Bayesian networks" @default.
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• W2558549895 hasPublicationYear "2007" @default.
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