## simple, scalable and accurate posterior interval estimation

“There is a lack of simple and scalable algorithms for uncertainty quantification.”

**A** paper by Cheng Li , Sanvesh Srivastava, and David Dunson that I had missed and which was pointed out on Andrew’s blog two days ago. As recalled in the very first sentence of the paper, above, the existing scalable MCMC algorithms somewhat fail to account for confidence (credible) intervals. In the sense that handling parallel samples does not naturally produce credible intervals.Since the approach is limited to one-dimensional quantity of interest, ζ=h(θ), the authors of the paper consider the MCMC approximations of the cdf of the said quantity ζ based on the manageable subsets like as many different approximations of the same genuine posterior distribution of that quantity ζ. (Corrected by a power of the likelihood but dependent on the particular subset used for the estimation.) The estimate proposed in the paper is a Wasserstein barycentre of the available estimations, barycentre that is defined as minimising the sum of the Wasserstein distances to all estimates. *(Why should this measure be relevant: the different estimates may be of different quality).* Interestingly (at least at a computational level), the solution is such that the quantile function of the Wasserstein barycentre is the average of the estimated quantiles functions. *(And is there an alternative loss returning the median cdf?)* A confidence interval based on the quantile function can then be directly derived. The paper shows that this Wasserstein barycentre converges to the true (marginal) posterior as the sample size m of each sample grows to infinity (and faster than 1/√m), with the strange side-result that the convergence is in 1/√n when the MLE of the global parameter θ is unbiased. Strange to me because unbiasedness is highly dependent on parametrisation while the performances of this estimator should not be, i.e., should be invariant under reparameterisation. Maybe this is due to ζ being a linear transform of θ in the convergence theorem… In any case, I find this question of merging cdf’s from poorly defined approximations to an unknown cdf of the highest interest and look forward any further proposal to this effect!

*Related*

This entry was posted on July 6, 2016 at 12:16 am and is filed under Statistics with tags embarassingly parallel, MCMC, Monte Carlo Statistical Methods, quantile function, scalability, scalable MCMC, tall data, Wasserstein distance. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.

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