%0 Journal Article %1 gregor2009generic %A Heinrich, Gregor %D 2009 %J Machine Learning and Knowledge Discovery in Databases %K models topic %P 517--532 %T A Generic Approach to Topic Models %U http://dx.doi.org/10.1007/978-3-642-04180-8_51 %X 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.

@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.}, added-at = {2009-10-23T12:02:34.000+0200}, author = {Heinrich, Gregor}, biburl = {https://www.bibsonomy.org/bibtex/2dd184722f6239798835404daa73f9d36/folke}, description = {SpringerLink - Book Chapter}, interhash = {9509ac0016837f04415cfcb5ef2ea93c}, intrahash = {dd184722f6239798835404daa73f9d36}, journal = {Machine Learning and Knowledge Discovery in Databases}, keywords = {models topic}, pages = {517--532}, timestamp = {2009-10-23T12:02:34.000+0200}, title = {A Generic Approach to Topic Models}, url = {http://dx.doi.org/10.1007/978-3-642-04180-8_51}, year = 2009 }