G. Heinrich. Machine Learning and Knowledge Discovery in Databases(2009)
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 compactalternative to Bayesian networks. Besides illustrating the interconnection of mixtures in topic models, the benefit of thisrepresentation is its straight-forward mapping to inference equations and algorithms, which is shown with the derivation andimplementation of a generic Gibbs sampling algorithm.