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a pseudo-marginal perspective on the ABC algorithm

May 5, 2014

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 […]

the ABC-SubSim algorithm

April 29, 2014

In 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, 2021

A 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, 2021

Research 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 […]

ABC in Svalbard [#2]

April 14, 2021

The second day of the ABC wwworkshop got a better start than yesterday [for me] as I managed to bike to Dauphine early enough to watch the end of Gael’s talk and Matias Quiroz’ in full on the Australian side (of zoom). With an interesting take on using frequency-domain (pseudo-)likelihoods in complex models. Followed by […]