Archive for permABC
congrats, Doctor Luciano!
Posted in Books, Kids, Statistics, University life with tags ABC, Bayesian computation, Gibbs sampling, insufficient statistic, mad, Paris, permABC, PhD thesis, postdoctoral position, Université Paris Dauphine, University of California Berkeley on January 23, 2026 by xi'an
mostly Monte Carlo, the return²⁵
Posted in pictures, Statistics, University life with tags ABC, CEREMADE, COVID-19, differential privacy, MCMC, Monte Carlo Statistical Methods, Paris Sciences et Lettres, PariSanté campus, permABC, permutations, PSL, seminar, SIR, Université Paris Dauphine, Wasserstein distance on October 9, 2025 by xi'an
Our local Mostly (and monthly) Monte Carlo seminar is back for a new academic year, now organized by Antoine Luciano and Timothy Johnston. The first session will take place at the PariSanté Campus on Friday 17 October 2025 (3:00pm, room 07), with the organisers opening the dance, with two talks:
3pm Timothy Johnston (CEREMADE, Université Paris Dauphine–PSL): Differential Privacy of Markov Chains
Joint work with Andrea Bertazzi, Alain Durmus and Gareth Roberts
In this talk we shall discuss differential privacy, a framework for quantifying the extent to which a random output depends on the information used to produce it. After introducing several related definition of differential privacy, we shall discuss techniques used to show the differential privacy of both trajectories and single draws from Markov Chains. In doing so we shall touch on a perturbation technique which allows for Wasserstein type bounds to be converted into stronger distances like the KL and Renyi divergence.
4pm Antoine Luciano (CEREMADE, Université Paris Dauphine–PSL): Permutations accelerate Approximate Bayesian Computation
Joint work with Charly Andral, Christian P. Robert and Robin J. Ryder
Approximate Bayesian Computation (ABC) methods have become essential tools for performing inference when likelihood functions are intractable or computationally prohibitive. However, their scalability remains a major challenge in hierarchical or high-dimensional models. In this paper, we introduce permABC, a new ABC framework designed for settings with both global and local parameters, where observations are grouped into exchangeable compartments. Building upon the Sequential Monte Carlo ABC (ABC-SMC) framework, permABC exploits the exchangeability of compartments through permutation-based matching, significantly improving computational efficiency. We then develop two further, complementary sequential strategies: Over Sampling, which facilitates early-stage acceptance by temporarily increasing the number of simulated compartments, and Under Matching, which relaxes the acceptance condition by matching only subsets of the data. These techniques allow for robust and scalable inference even in high-dimensional regimes. Through synthetic and real-world experiments – including a hierarchical Susceptible-Infectious-Recover model of the early COVID-19 epidemic across 94 French departments – we demonstrate the practical gains in accuracy and efficiency achieved by our approach.
permutations accelerate ABC!
Posted in Books, Kids, pictures, Statistics, University life with tags ABC, accelerated ABC, Approximate Bayesian computation, Bayesian inference, book cover, COVID-19, David Spiegelhalter, France, Imperial College London, PariSanté campus, permABC, permutations, PhD students, PSL Research University, recovery rate, sequential ABC, SIR, subsampling, Susceptible–Infectious–Recovered model, Sylvia Richardson, The Prairie Chair, Université Paris Dauphine, Wally Gilks on July 9, 2025 by xi'an
Yesterday a arXival by Antoine Luciano, Charly Andral (both PhD students, now or then, at Paris Dauphine), Robin Ryder (formerly at Paris Dauphine, now at Imperial College London) and myself got posted. It proposes to improve the scalability of ABC methods by exploiting the (full or partial) exchangeability in the data by implementing permutation-based matching between observed and simulated samples. This significantly improves computational efficiency, which is further enhanced by sequential strategies such as over-sampling, which facilitates early-stage acceptance by temporarily increasing the number of simulated compartments, and under-matching, which relaxes the acceptance condition by matching only subsets of the data. The map of France appears in connection with an application of the method to estimating SIR parameters, department by department. (It is also reminding me of the cover of Markov Chain Monte Carlo methods in practice, the 1996 contributed book edited by Wally Gilks, Sylvia Richardson and David Spiegelhalter.)
