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

Streaming Gibbs Sampling for LDA Model