My friends Luke Bornn, Natesh Pillai and Dawn Woodard just arXived along with Aaron Smith a short note on the convergence properties of ABC. When compared with acceptance-rejection or regular MCMC. Unsurprisingly, ABC does worse in both cases. What is central to this note is that ABC can be (re)interpreted as a pseudo-marginal method where […]

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## the ABC-SubSim algorithm

April 29, 2014In a nice coincidence with my ABC tutorial at AISTATS 2014 – MLSS, Manuel Chiachioa, James Beck, Juan Chiachioa, and Guillermo Rus arXived today a paper on a new ABC algorithm, called ABC-SubSim. The SubSim stands for subset simulation and corresponds to an approach developed by one of the authors for rare-event simulation. This approach […]

## conditioning an algorithm

June 25, 2021A question of interest on X validated: given a (possibly black-box) algorithm simulating from a joint distribution with density [wrt a continuous measure] p(z,y) (how) is it possible to simulate from the conditional p(y|z⁰)? Which reminded me of a recent paper by Lindqvist et al. on conditional Monte Carlo. Which zooms on the simulation of […]

## ABC on brain networks

April 16, 2021Research Gate sent me an automated email pointing out a recent paper citing some of our ABC papers. The paper is written by Timothy West et al., neuroscientists in the UK, comparing models of Parkinsonian circuit dynamics. Using SMC-ABC. One novelty is the update of the tolerance by a fixed difference, unless the acceptance rate […]