Category: ABC

unimaginable scale culling

Despite the evidence brought by ABC on the inefficiency of culling in massive proportions the British Isles badger population against bovine tuberculosis, the [sorry excuse for a] United Kingdom government has permitted a massive expansion of badger culling, with up to 64,000 animals likely to be killed this autumn… Since the cows are the primary […]

likelihood-free inference by ratio estimation

“This approach for posterior estimation with generative models mirrors the approach of Gutmann and Hyvärinen (2012) for the estimation of unnormalised models. The main difference is that here we classify between two simulated data sets while Gutmann and Hyvärinen (2012) classified between the observed data and simulated reference data.” A 2018 arXiv posting by Owen […]

off to SimStat2019, Salzburg

Today, I am off to Salzburg for the SimStat 2019 workshop, or more formally the 10th International Workshop on Simulation and Statistics, where I give a talk on ABC. The program of the workshop is quite diverse and rich and so I do not think I will have time to take advantage of the Hohe […]

delayed-acceptance. ADA boosted

Samuel Wiqvist and co-authors from Scandinavia have recently arXived a paper on a new version of delayed acceptance MCMC. The ADA in the novel algorithm stands for approximate and accelerated, where the approximation in the first stage is to use a Gaussian process to replace the likelihood. In our approach, we used subsets for partial […]

a problem that did not need ABC in the end

While in Denver, at JSM, I came across [across validated!] this primarily challenging problem of finding the posterior of the 10³ long probability vector of a Multinomial M(10⁶,p) when only observing the range of a realisation of M(10⁶,p). This sounded challenging because the distribution of the pair (min,max) is not available in closed form. (Although […]

unbiased product of expectations

While I was not involved in any way, or even aware of this research, Anthony Lee, Simone Tiberi, and Giacomo Zanella have an incoming paper in Biometrika, and which was partly written while all three authors were at the University of Warwick. The purpose is to design an efficient manner to approximate the product of […]

Introductory overview lecture: the ABC of ABC [JSM19 #1]

Here are my slides [more or less] for the introductory overview lecture I am giving today at JSM 2019, 4:00-5:50, CC-Four Seasons I. There is obviously quite an overlap with earlier courses I gave on the topic, although I refrained here from mentioning any specific application (like population genetics) to focus on statistical and computational […]

off to Denver! [JSM2019]

As off today, I am attending JSM 2019 in Denver, giving an “Introductory Overview Lecture” on The ABC of Approximate Bayesian Computation on Sunday afternoon and chairing an ABC session on Monday morning. As far as I know these are the only ABC sessions at JSM this year… And hence the only sessions I will […]

uncertainty in the ABC posterior

In the most recent Bayesian Analysis, Marko Järvenpää et al. (including my coauthor Aki Vehtari) consider an ABC setting where the number of available simulations of pseudo-samples  is limited. And where they want to quantify the amount of uncertainty resulting from the estimation of the ABC posterior density. Which is a version of the Monte […]

locusts in a random forest

My friends from Montpellier, where I am visiting today, Arnaud Estoup, Jean-Michel Marin, and Louis Raynal, along with their co-authors, have recently posted on biorXiv a paper using ABC-RF (Random Forests) to analyse the divergence of two populations of desert locusts in Africa. (I actually first heard of their paper by an unsolicited email from […]

ateliers statistiques bayésiens

The French Statistical Association is running a training workshop on practical computational Bayesian methods on 10-12 September 2019 in Paris (IHP), animated by Sylvain LE CORFF (Telecom SudParis – Institut Polytechnique de Paris) for the initia…

likelihood-free approximate Gibbs sampling

“Low-dimensional regression-based models are constructed for each of these conditional distributions using synthetic (simulated) parameter value and summary statistic pairs, which then permit approximate Gibbs update steps (…) synthetic datasets are not generated during each sampler iteration, thereby providing efficiencies for expensive simulator models, and only require sufficient synthetic datasets to adequately construct the full […]

talk at CISEA 2019

Here are my slides for the overview talk I am giving at CISEA 2019, in Abidjan, highly resemblant with earlier talks, except for the second slide!

A precursor of ABC-Gibbs

Following our arXival of ABC-Gibbs, Dennis Prangle pointed out to us a 2016 paper by Athanasios Kousathanas, Christoph Leuenberger, Jonas Helfer, Mathieu Quinodoz, Matthieu Foll, and Daniel Wegmann, Likelihood-Free Inference in High-Dimensional Model, published in Genetics, Vol. 203, 893–904 in June 2016. This paper contains a version of ABC Gibbs where parameters are sequentially simulated […]

ABC with Gibbs steps

With Grégoire Clarté, Robin Ryder and Julien Stoehr, all from Paris-Dauphine, we have just arXived a paper on the specifics of ABC-Gibbs, which is a version of ABC where the generic ABC accept-reject step is replaced by a sequence of n conditional ABC accept-reject steps, each aiming at an ABC version of a conditional distribution […]

postdoc position still open

The post-doctoral position supported by the ANR funding of our Paris-Saclay-Montpellier research conglomerate on approximate Bayesian inference and computation remains open for the time being. We are more particularly looking for candidates with a strong background in mathematical statistics, esp. Bayesian non-parametrics, towards the analysis of the limiting behaviour of approximate Bayesian inference. Candidates should […]

selecting summary statistics [a tale of two distances]

As Jonathan Harrison came to give a seminar in Warwick [which I could not attend], it made me aware of his paper with Ruth Baker on the selection of summaries in ABC. The setting is an ABC-SMC algorithm and it relates with Fearnhead and Prangle (2012), Barnes et al. (2012), our own random forest approach, […]

ABC in Grenoble, 19-20 March 2020

The next occurrence of the “ABC in…” workshops will take place in Grenoble, France, on 19-20 March 2020. Both local organising and international scientific committees have been constituted and the program should soon be constructed, along with calls to contributions launched at the same time. As in most earlier versions of the workshops (ABC in […]

robust Bayesian synthetic likelihood

David Frazier (Monash University) and Chris Drovandi (QUT) have recently come up with a robustness study of Bayesian synthetic likelihood that somehow mirrors our own work with David. In a sense, Bayesian synthetic likelihood is definitely misspecified from the start in assuming a Normal distribution on the summary statistics. When the data generating process is […]