In their original work <cite>Mimno et al. (2009)</cite> used the Gibbs sampling approach as a posterior inference algorithm to assign topics distributions over their test collection. While more straightforward to implement, this sampling approach is inherently slow when applied to large collections which makes the original PLTM work practically infeasible to be used on real-world data sets. In this paper we use Hoffman et al. (2010) approach.