Archive for data partitioning

EP as a way of life (aka Life of EP)

Posted in Books, Statistics, University life with tags , , , , , , , on December 24, 2014 by xi'an

When Andrew was in Paris, we discussed at length about using EP for handling big datasets in a different way than running parallel MCMC. A related preprint came out on arXiv a few days ago, with an introduction on Andrews’ blog. (Not written two months in advance as most of his entries!)

The major argument in using EP in a large data setting is that the approximation to the true posterior can be build using one part of the data at a time and thus avoids handling the entire likelihood function. Nonetheless, I still remain mostly agnostic about using EP and a seminar this morning at CREST by Guillaume Dehaene and Simon Barthelmé (re)generated self-interrogations about the method that hopefully can be exploited towards the future version of the paper.

One of the major difficulties I have with EP is about the nature of the resulting approximation. Since it is chosen out of a “nice” family of distributions, presumably restricted to an exponential family, the optimal approximation will remain within this family, which further makes EP sound like a specific variational Bayes method since the goal is to find the family member the closest to the posterior in terms of Kullback-Leibler divergence. (Except that the divergence is the opposite one.) I remain uncertain about what to do with the resulting solution, as the algorithm does not tell me how close this solution will be from the true posterior. Unless one can use it as a pseudo-distribution for indirect inference (a.k.a., ABC)..?

Another thing that became clear during this seminar is that the decomposition of the target as a product is completely arbitrary, i.e., does not correspond to an feature of the target other than the later being the product of those components. Hence, the EP partition could be adapted or even optimised within the algorithm. Similarly, the parametrisation could be optimised towards a “more Gaussian” posterior. This is something that makes EP both exciting as opening many avenues for experimentation and fuzzy as its perceived lack of goal makes comparing approaches delicate. For instance, using MCMC or HMC steps to estimate the parameters of the tilted distribution is quite natural in complex settings but the impact of the additional approximation must be gauged against the overall purpose of the approach.