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"NIPS Neural Information Processing Systems 8-11th December 2014, Montreal, Canada",,, "Paper ID:","1711" "Title:","Distributed Bayesian Posterior Sampling via Moment Sharing" Current Reviews First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper proposes a new distributed Bayesian Posterior inference algorithm for big data, an important problem that has garnered a lot of attention in the last few years. The goal is to sample from the posterior distribution of model parameters theta given a dataset that is divided among m machines. The proposed distributed algorithm runs a separate Markov chain on each machine, each of which samples from a distribution proportional to p(Dm|theta) qm(theta) where Dm is the local data subset on machine m and qm is a variational (Gaussian) approximation to the product of similar factors on other machines. I think this is a great idea and addresses a very important problem. The paper is also relatively easy to understand. However, the experimental section is quite weak which makes me a little hesitant to argue strongly for acceptance.