Correlated Topic Models are a great advance on the original topic model - see Blei and Lafferty 2007 for more info.
My question is this - how does a Correlated Topic Model impact the overall distribution of topics across all documents when compared with a normal topic model? With a normal topic model (with a symmetric Dirichlet prior) the distribution of topics can be remarkably uniform, which is not always appropriate. As discussed by Wallach et al. having asymmetric priors is key to having better model fit and interpretability - it would be great if the Correlated Topic Model implicitly addressed this issue by creating a topic distribution which is less uniform.