Infinite State Bayes-Nets for Structured Domains

Welling, Max, Porteous, Ian, Bart, Evgeniy

Neural Information Processing Systems 

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.

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