  Gibbs sampling is a Markov chain Monte Carlo technique commonly used for
estimating marginal distributions. To speed up Gibbs sampling, there has
recently been interest in parallelizing it by executing asynchronously. While
empirical results suggest that many models can be efficiently sampled
asynchronously, traditional Markov chain analysis does not apply to the
asynchronous case, and thus asynchronous Gibbs sampling is poorly understood.
In this paper, we derive a better understanding of the two main challenges of
asynchronous Gibbs: bias and mixing time. We show experimentally that our
theoretical results match practical outcomes.
