  A single, stationary topic model such as latent Dirichlet allocation is
inappropriate for modeling corpora that span long time periods, as the
popularity of topics is likely to change over time. A number of models that
incorporate time have been proposed, but in general they either exhibit limited
forms of temporal variation, or require computationally expensive inference
methods. In this paper we propose non-parametric Topics over Time (npTOT), a
model for time-varying topics that allows an unbounded number of topics and
exible distribution over the temporal variations in those topics' popularity.
We develop a collapsed Gibbs sampler for the proposed model and compare against
existing models on synthetic and real document sets.
