  Streaming variational Bayes (SVB) is successful in learning LDA models in an
online manner. However previous attempts toward developing online Monte-Carlo
methods for LDA have little success, often by having much worse perplexity than
their batch counterparts. We present a streaming Gibbs sampling (SGS) method,
an online extension of the collapsed Gibbs sampling (CGS). Our empirical study
shows that SGS can reach similar perplexity as CGS, much better than SVB. Our
distributed version of SGS, DSGS, is much more scalable than SVB mainly because
the updates' communication complexity is small.
