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ABC for big data

June 23, 2015

“The results in this paper suggest that ABC can scale to large data, at least for models with a xed number of parameters, under the assumption that the summary statistics obey a central limit theorem.” In a week rich with arXiv submissions about MCMC and “big data”, like the Variational consensus Monte Carlo of Rabinovich […]

ABC in…?

June 4, 2015

I take the opportunity of this abc picture taken in Apulia, by friends from Milano, to make a late call for the next European “ABC in”! After the Paris, London, and Roma occurrences, there are still heaps of European cities to hold a one- or two-day workshop on the latest developments on approximate Bayesian computing. […]

approximate maximum likelihood estimation using data-cloning ABC

June 2, 2015

“By accepting of having obtained a poor approximation to the posterior, except for the location of its main mode, we switch to maximum likelihood estimation.” Presumably the first paper ever quoting from the ‘Og! Indeed, Umberto Picchini arXived a paper about a technique merging ABC with prior feedback (rechristened data cloning by S. Lele), where […]

ABC and cosmology

May 4, 2015

Two papers appeared on arXiv in the past two days with the similar theme of applying ABC-PMC [one version of which we developed with Mark Beaumont, Jean-Marie Cornuet, and Jean-Michel Marin in 2009] to cosmological problems. (As a further coincidence, I had just started refereeing yet another paper on ABC-PMC in another astronomy problem!) The […]

extending ABC to high dimensions via Gaussian copula

April 28, 2015

Li, Nott, Fan, and Sisson arXived last week a new paper on ABC methodology that I read on my way to Warwick this morning. The central idea in the paper is (i) to estimate marginal posterior densities for the components of the model parameter by non-parametric means; and (ii) to consider all pairs of components […]


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