@article{gregor2009generic,
  abstract = {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.},
  author = {Heinrich, Gregor},
  interhash = {9509ac0016837f04415cfcb5ef2ea93c},
  intrahash = {dd184722f6239798835404daa73f9d36},
  journal = {Machine Learning and Knowledge Discovery in Databases},
  pages = {517--532},
  title = {A Generic Approach to Topic Models},
  url = {http://dx.doi.org/10.1007/978-3-642-04180-8_51},
  year = 2009
}