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## Variational Inference  Developing the full update rules for the variational inference model is beyond the scope of this paper. However, if What  we we can still have opted to do instead is attempt to reformulate approaches used for similar models.  The work of Wang et al. \cite{Wang2011} and Bryant et al.\cite{Bryant2012} on variational inference is a step in this direction. They develop a variational framework for the hierarchal dirichlet model, HDP,  a fundamental part of all nonparametric our  TLG formulations.As such we seek to build on their work and apply it to specifically the TLG case. Our goal is motivated by the excellent performance of variational inference. This is both generally \cite{Grimmer_2010} and specifically; Wang et al.\cite{Wang2011} had excellent performance on a dataset of 400,000 articles, an order of magnitude larger than any sampling-based inference on the TLG problem.  As such we build on their work and apply it to specifically the TLG case.