Variational Bayes for Generic Topic Models

The article contributes a derivation of variational Bayes for a large class of topic models by generalising from the well-known model of latent Dirichcation. For an abstraction of these models as systems of interconnected mixtures, variational update equations are obtained, leading to inference algorithms for models that so far have used Gibbs sampling exclusively.

• Type: Conference
• Year: 2009
• Where: KI
• Authors: Gregor Heinrich, Michael Goesele
• Keywords: Artificial Intelligence, KI 2009, Topic Models, Variational Bayes, Variational Update Equations