Archive for approximate Bayesian inference

invited sessions at BayesComp [submissions open]

Posted in Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , on July 8, 2022 by xi'an

The call for invited sessions at BayesComp 2022 is now open, submission can be done via Google Form. Submissions are due by August 7. If you have any questions, please email to bayescomp2023 on Gmail. And if you want to contribute to AG::DC, please email Anto, Heikki or me!

nonparametric ABC [seminar]

Posted in pictures, Statistics, University life with tags , , , , , , , , , , , , , on June 3, 2022 by xi'an

Puzzle: How do you run ABC when you mistrust the model?! We somewhat considered this question in our misspecified ABC paper with David and Judith. An AISTATS 2022 paper by Harita Dellaporta (Warwick), Jeremias KnoblauchTheodoros Damoulas (Warwick), and François-Xavier Briol (formerly Warwick) is addressing this same question and Harita presented the paper at the One World ABC webinar yesterday.

It is inspired from Lyddon, Walker & Holmes (2018), who place a nonparametric prior on the generating model, in top of the assumed parametric model (with an intractable likelihood). This induces a push-forward prior on the pseudo-true parameter, that is, the value that brings the parametric family the closest possible to the true distribution of the data. Here defined as a minimum distance parameter, the maximum mean discrepancy (MMD). Choosing RKHS framework allows for a practical implementation, resorting to simulations for posterior realisations from a Dirichlet posterior and from the parametric model, and stochastic gradient for computing the pseudo-true parameter, which may prove somewhat heavy in terms of computing cost.

The paper also containts a consistency result in an ε-contaminated setting (contamination of the assumed parametric family). Comparisons like the above with a fully parametric Wasserstein-ABC approach show that this alter resists better misspecification, as could be expected since the later is not constructed for that purpose.

Next talk is on 23 June by Cosma Shalizi.

Recent Advances in Approximate Bayesian Inference [YSE, 15.2.22]

Posted in Statistics, University life with tags , , , , , on May 11, 2022 by xi'an


On June 15, the Young Statisticians Europe initiative is organising an on-line seminar on approximate Bayesian inference. With talks by

starting at 7:00 PT / 10:00 EST / 16:00 CET. The registration form is available here.

Concentration and robustness of discrepancy-based ABC [One World ABC ‘minar, 28 April]

Posted in Statistics, University life with tags , , , , , , , , , , , on April 15, 2022 by xi'an

Our next speaker at the One World ABC Seminar will be Pierre Alquier, who will talk about “Concentration and robustness of discrepancy-based ABC“, on Thursday April 28, at 9.30am UK time, with an abstract reported below.
Approximate Bayesian Computation (ABC) typically employs summary statistics to measure the discrepancy among the observed data and the synthetic data generated from each proposed value of the parameter of interest. However, finding good summary statistics (that are close to sufficiency) is non-trivial for most of the models for which ABC is needed. In this paper, we investigate the properties of ABC based on integral probability semi-metrics, including MMD and Wasserstein distances. We exhibit conditions ensuring the contraction of the approximate posterior. Moreover, we prove that MMD with an adequate kernel leads to very strong robustness properties.

Big Bayes postdoctoral position in Oxford [UK]

Posted in Statistics with tags , , , , , , , , , , , on March 3, 2022 by xi'an

Forwarding a call for postdoctoral applications from Prof Judith Rousseau, with deadline 30 March:

Seeking a Postdoctoral Research Assistant, to join our group at the Department of Statistics. The Postdoctoral Research Assistant will be carrying out research for the ERC project General Theory for Big Bayes, reporting to Professor Judith Rousseau. They will provide guidance to junior members of the research group such as PhD students, and/or project volunteers.

The aim of this project is to develop a general theory for the analysis of Bayesian methods in complex and high (or infinite) dimensional models which will cover not only fine understanding of the posterior distributions but also an analysis of the output of the algorithms used to implement the approaches. The main objectives of the project are (briefly): 1) Asymptotic analysis of the posterior distribution of complex high dimensional models 2) Interactions between the asymptotic theory of high dimensional posterior distributions and computational complexity. We will also enrich these theoretical developments by 3) strongly related domains of applications, namely neuroscience, terrorism and crimes, and ecology.

The postholder will hold or be close to completion of a PhD/DPhil in statistics together with relevant experience. They will have the ability to manage own academic research and associated activities and have previous experience of contributing to publications/presentations. They will contribute ideas for new research projects and research income generation. Ideally, the postholder will also have experience in theoretical properties of Bayesian procedures and/or approximate Bayesian methods.

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