  Inference is an integral part of probabilistic topic models, but is often
non-trivial to derive an efficient algorithm for a specific model. It is even
much more challenging when we want to find a fast inference algorithm which
always yields sparse latent representations of documents. In this article, we
introduce a simple framework for inference in probabilistic topic models,
denoted by FW. This framework is general and flexible enough to be easily
adapted to mixture models. It has a linear convergence rate, offers an easy way
to incorporate prior knowledge, and provides us an easy way to directly trade
off sparsity against quality and time. We demonstrate the goodness and
flexibility of FW over existing inference methods by a number of tasks.
Finally, we show how inference in topic models with nonconjugate priors can be
done efficiently.
