(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Cheng Li, Sanvesh Srivastava, and David Dunson write:
We propose a new scalable algorithm for posterior interval estimation. Our algorithm first runs Markov chain Monte Carlo or any alternative posterior sampling algorithm in parallel for each subset posterior, with the subset posteriors proportional to the prior multiplied by the subset likelihood raised to the full data sample size divided by the subset sample size. To obtain an accurate estimate of a posterior quantile for any one-dimensional functional of interest, we simply calculate the quantile estimates in parallel for each subset posterior and then average these estimates.
Wow—does this really work? This could be awesome. Definitely worth trying out in Stan.
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