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 […]

# Category: ABC

## 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 […]

## the true meaning of ABC

## MCMC importance samplers for intractable likelihoods

Jordan Franks just posted on arXiv his PhD dissertation at the University of Jyväskylä, where he discuses several of his works: M. Vihola, J. Helske, and J. Franks. Importance sampling type estimators based on approximate marginal MCMC. Preprint arXiv:1609.02541v5, 2016. J. Franks and M. Vihola. Importance sampling correction versus standard averages of reversible MCMCs in […]

## over-confident about mis-specified models?

Ziheng Yang and Tianqui Zhu published a paper in PNAS last year that criticises Bayesian posterior probabilities used in the comparison of models under misspecification as “overconfident”. The paper is written from a phylogeneticist point of view, rather than from a statistician’s perspective, as shown by the Editor in charge of the paper [although I […]

## over-confident about mis-specified models?

Ziheng Yang and Tianqui Zhu published a paper in PNAS last year that criticises Bayesian posterior probabilities used in the comparison of models under misspecification as “overconfident”. The paper is written from a phylogeneticist point of view, rather than from a statistician’s perspective, as shown by the Editor in charge of the paper [although I […]

## holistic framework for ABC

An AISTATS 2019 paper was recently arXived by Kelvin Hsu and Fabio Ramos. Proposing an ABC method “…consisting of (1) a consistent surrogate likelihood model that modularizes queries from simulation calls, (2) a Bayesian learning objective for hyperparameters that improves inference accuracy, and (3) a posterior surrogate density and a super-sampling inference algorithm using its […]

## adaptive copulas for ABC

A paper on ABC I read on my way back from Cambodia: Yanzhi Chen and Michael Gutmann arXived an ABC [in Edinburgh] paper on learning the target via Gaussian copulas, to be presented at AISTATS this year (in Okinawa!). Linking post-processing (regression) ABC and sequential ABC. The drawback in the regression approach is that the […]

## asymptotics of synthetic likelihood [a reply from the authors]

[Here is a reply from David, Chris, and Robert on my earlier comments, highlighting some points I had missed or misunderstood.] Dear Christian Thanks for your interest in our synthetic likelihood paper and the thoughtful comments you wrote about it on your blog. We’d like to respond to the comments to avoid some misconceptions. Your […]

## absint[he] post-doc on approximate Bayesian inference in Paris, Montpellier and Oxford

As a consequence of its funding by the Agence Nationale de la Recherche (ANR) in 2018, the ABSint research conglomerate is now actively recruiting a post-doctoral collaborator for up to 24 months. The accronym ABSint stands for Approximate Bayesian solutions for inference on large datasets and complex models. The ABSint conglomerate involves researchers located in […]

## asymptotics of synthetic likelihood

David Nott, Chris Drovandi and Robert Kohn just arXived a paper on a comparison between ABC and synthetic likelihood, which is both interesting and timely given that synthetic likelihood seems to be lacking behind in terms of theoretical evaluation. I am however as puzzled by the results therein as I was by the earlier paper […]

## auxiliary likelihood ABC in print

Our paper with Gael Martin, Brendan McCabe , David Frazier and Worapree Maneesoonthorn, with full title Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models, has now appeared in JCGS. To think that it started in Rimini in 2009, when I met Gael for the first time at the Rimini Bayesian Econometrics conference, although we […]

## particular degeneracy in ABC model choice

In one of the presentations by the last cohort of OxWaSP students, the group decided to implement an ABC model choice strategy based on sequential ABC inspired from Toni et al. (2008). and this made me reconsider this approach (disclaimer: no criticism of the students implied in the following!). Indeed, the outcome of the simulation […]

## Bayesian intelligence in Warwick

This is an announcement for an exciting CRiSM Day in Warwick on 20 March 2019: with speakers 10:00-11:00 Xiao-Li Meng (Harvard): “Artificial Bayesian Monte Carlo Integration: A Practical Resolution to the Bayesian (Normalizing Constant) Paradox” 11:00-12:00 Julien Stoehr (Dauphine): “Gibbs sampling and ABC” 14:00-15:00 Arthur Ulysse Jacot-Guillarmod (École Polytechnique Fedérale de Lausanne): “Neural Tangent Kernel: […]

## Bayesian intelligence in Warwick

This is an announcement for an exciting CRiSM Day in Warwick on 20 March 2019: with speakers 10:00-11:00 Xiao-Li Meng (Harvard): “Artificial Bayesian Monte Carlo Integration: A Practical Resolution to the Bayesian (Normalizing Constant) Paradox” 11:00-12:00 Julien Stoehr (Dauphine): “Gibbs sampling and ABC” 14:00-15:00 Arthur Ulysse Jacot-Guillarmod (École Polytechnique Fedérale de Lausanne): “Neural Tangent Kernel: […]

## a pen for ABC

Among the flury of papers arXived around the ICML 2019 deadline, I read on my way back from Oxford a paper by Wiqvist et al. on learning summary statistics for ABC by neural nets. Pointing out at another recent paper by Jiang et al. (2017, Statistica Sinica) which constructed a neural network for predicting each […]

## information maximising neural networks summaries

After missing the blood moon eclipse last night, I had a meeting today at the Paris observatory (IAP), where we discussed an ABC proposal made by Tom Charnock, Guilhem Lavaux, and Benjamin Wandelt from this institute. “We introduce a simulation-based machine learning technique that trains artificial neural networks to find non-linear functionals of data that […]

## Computational Bayesian Statistics [book review]

This Cambridge University Press book by M. Antónia Amaral Turkman, Carlos Daniel Paulino, and Peter Müller is an enlarged translation of a set of lecture notes in Portuguese. (Warning: I have known Peter Müller from his PhD years in Purdue University and cannot pretend to perfect objectivity. For one thing, Peter once brought me frozen-solid […]

## prepaid ABC

Merijn Mestdagha, Stijn Verdoncka, Kristof Meersa, Tim Loossensa, and Francis Tuerlinckx from the KU Leuven, some of whom I met during a visit to its Wallon counterpart Louvain-La-Neuve, proposed and arXived a new likelihood-free approach based on saving simulations on a large scale for future users. Future users interested in the same model. The very […]

## a book and three chapters on ABC

In connection with our handbook on mixtures being published, here are three chapters I contributed to from the Handbook of ABC, edited by Scott Sisson, Yanan Fan, and Mark Beaumont: 6. Likelihood-free Model Choice, by J.-M. Marin, P. Pudlo, A. Estoup and C.P. Robert 12. Approximating the Likelihood in ABC, by C. C. Drovandi, C. […]