high performance computing on graphics processing units: hgpu.org

This article contributes a generic model of topic models. To define the problem space, general characteristics for this class of models are derived, which give rise to a representation of topic models as "mixture networks", a domain-specific compact alternative to Bayesian networks. Besides illustrating the interconnection of mixtures in topic models, the benefit of this representation is its straight-forward mapping to inference equations and algorithms, which is shown with the derivation and implementation of a generic Gibbs sampling algorithm.