[alextp]

ML PhD student here. The reason why this is different is that the parallel monte carlo simulations are running on different subsets of the data in each machine, and then averaged.

It is not obvious that this can work at all in some cases. Think, for example, a clustering model. If there are two clusters, but one machine calls them A B and the other machine calls them B A, averaging will give you useless results.

So the contribution of this paper is finding a set of models on which naive averaging works, and showing an efficient mapreduce implementation of it.

That said, I don't find the paper particularly interesting.

[christopheraden]

Ph.D student in Stats here. That we can even sample the full posterior with so much data is interesting in and of itself. The method isn't particularly revolutionary, especially compared to a method Zaiyang Huang and Andy Gelman[1] came up with 8 years ago, but it's practical (for a restricted class of models). Steve Scott spoke to my department about a month ago, describing the problem. It's not a solution that works for all posteriors, but it certainly allows more freedom than restricting oneself to conjugate priors and allows computation on large datasets.

Academics tend to trivialize the implementation (we had some pretty strong critics of his talk in my dept), but some kudos are in order for that, even if the algorithm itself isn't revolutionary.

[1]: http://www.stat.columbia.edu/~gelman/research/unpublished/co...